Welcome to IDM tuberculosis modeling¶

The Institute for Disease Modeling (IDM) develops complex software modeling. The primary software, Epidemiological MODeling software (EMOD), helps determine the combination of health policies and intervention strategies that can lead to disease eradication. We share this modeling software with the research community to advance the understanding of disease dynamics.

Documentation structure¶

IDM recently separated the documentation into a training set that walks through tutorials that provide an introduction to disease modeling and disease-specific sets that provide guidance for researchers modeling particular diseases. For example, this documentation set includes general installation and usage instructions in addition to content specific to modeling tuberculosis and other airborne diseases.

“Using the model” contains information for researchers who want to create simulations of disease dynamics using EMOD as developed by IDM.

“Advancing the model” contains information for researchers and developers who want to modify the EMOD source code to add more functionality to the model.

What’s new¶

This topic describes new functionality and breaking changes for recently released versions of Epidemiological MODeling software (EMOD).

Contents

EMOD v2.14
EMOD v2.13
EMOD v2.10
- EMOD software upgrades

EMOD v2.14 ¶

The EMOD v2.14 milestone includes new configuration parameters for exposure skipping, maternal protection, and natural mortality.

Parameter	Data type	Minimum	Maximum	Default	Description
Enable_Demographics_Risk	boolean	0	1	0	Controls whether or not the simulation includes the impact of disease risk in demographics. This parameter is not used in this simulation type.
Enable_Immunity_Distribution	boolean	0	1	0	Controls whether or not individuals in the population have immunity at the beginning of the simulation. If set to 0, individuals are not initialized with immunity but may acquire immunity. If set to 1, you must indicate the type of distribution to use for immunity in the configuration parameter Immunity_Initialization_Distribution_Type and the distribution values in the demographics file. Enable_Immunity must be set to 1.
Enable_Initial_Prevalence	boolean	0	1	0	Controls whether or not parameters in the demographics file are used to define a distribution for the number of infected people per node at the beginning of the simulation. Set the distribution under NodeAttributes using PrevalenceDistributionFlag, PrevalenceDistribution1, and PrevalenceDistribution2. This parameter is not used in this simulation type.
Enable_Maternal_Antibodies_Transmission	boolean	0	1	0	Controls whether or not mothers pass antibodies to their infants. When enabled, you must set Maternal_Antibodies_Type,Maternal_Antibody_Protection, and Maternal_Antibody_Decay_Rate. Enable_Birth must be set to 1.
Enable_Maternal_Protection	boolean	0	1	0	Controls whether or not mothers pass immunity on to their infants. Setting to 1 (true) enables maternal protection as defined by Maternal_Protection_Type. Enable_Birth must be set to 1 (true).
Enable_Natural_Mortality	boolean	0	1	0	Controls whether or not individuals are removed from the simulation due to natural (non-disease) deaths. Enable_Vital_Dynamics must be set to 1 (true).
Enable_Skipping	boolean	0	1	0	Controls whether or not the simulation uses an optimization that can increase performance by up to 50% in some cases by probablistically exposing individuals rather than exposing every single person. Useful in low-prevalence, high-population scenarios. This parameter is not used in this simulation type.
Enable_Susceptibility_Scaling	boolean	0	1	0	Controls whether or not susceptibility is scaled by time as defined by Susceptibility_Scaling_Type.
Maternal_Linear_Slope	float	0.0001	1	0.01	Slope parameter describing the rate of waning for maternal protection, must be positive. The per-day increase in susceptibility. Maternal_Protection_Type must be set to LINEAR_FRACTIONAL or LINEAR_BINARY.
Maternal_Linear_SusZero	float	0	1	0.2	The initial level of maternal protection at age 0, given as susceptibility. A value of 0.0 implies total protection, a value of 1.0 implies no protection. Maternal_Protection_Type must be set to LINEAR_FRACTIONAL or LINEAR_BINARY.
Maternal_Protection_Type	enum	NA	NA	NONE	The type of maternal protection afforded to infants. Enable_Maternal_Protection must be set to 1 (true). Possible values are: NONE No immune protection is passed on to infants. LINEAR_FRACTIONAL Susceptibility is a function of age and equal susceptibility is assigned to all individuals. Susceptibility = Maternal_Linear_Slope * age + Maternal_Linear_SusZero LINEAR_BINARY Individuals are assigned an age cutoff before which they have reduced susceptibility and after which they do not. The age cutoff is randomly assigned to each individual. AgeCutoff = (RAND - Maternal_Linear_SusZero) / Maternal_Linear_Slope SIGMOID_FRACTIONAL Susceptibility is a function of age and equal susceptibility is assigned to all individuals. Susceptibility = Maternal_Sigmoid_SusInit + (1.0 - Maternal_Sigmoid_SusInit) / * (1.0 + EXP(( Maternal_Sigmoid_HalfMaxAge - age) / Maternal_Sigmoid_SteepFac)) SIGMOID_BINARY Individuals are assigned an age cutoff before which they have reduced susceptibility and after which they do not. The age cutoff is randomly assigned to each individual. AgeCutoff = ( Maternal_Sigmoid_HalfMaxAge - Maternal_Sigmoid_SteepFac * LOG (1.0 / RAND - 1.0 + 0.001)
Maternal_Sigmoid_HalfMaxAge	float	-270	3650	180	The age in days that the level of maternal protection is half of its initial value. Maternal_Protection_Type must be set to SIGMOID_FRACTIONAL or SIGMOID_BINARY.
Maternal_Sigmoid_SteepFac	float	0.1	1000	30	The steepness factor describing the rate of waning for maternal protection, must be positive. Small values imply rapid waning.Maternal_Protection_Type must be set to SIGMOID_FRACTIONAL or SIGMOID_BINARY.
Maternal_Sigmoid_SusInit	float	0	1	0	The initial level of maternal protection at age -INF, given as susceptibility. A value of 0.0 implies total protection, a value of 1.0 implies no protection. Maternal_Protection_Type must be set to SIGMOID_FRACTIONAL or SIGMOID_BINARY.
Susceptibility_Scaling_Rate	float	0	3.40282e+38	0	The scaling rate for the variation in time of the log-linear susceptibility scaling. Susceptibility_Scaling_Type must be set to LOG_LINEAR_FUNCTION_OF_TIME.
Susceptibility_Scaling_Type	enum	NA	NA	CONSTANT_SUSCEPTIBILITY	The effect of time on susceptibility. Possible values are: CONSTANT_SUSCEPTIBILITY LOG_LINEAR_FUNCTION_OF_TIME

EMOD v2.13 ¶

The EMOD v2.13 release includes many new features for all supported simulation types.

New configuration parameters ¶

For the tuberculosis simulation type, the following new configuration parameters are available:

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Custom_Reports_Filename	string	NA	NA	UNINITIALIZED STRING	The name of the file containing custom report configuration parameters. Omitting this parameter or setting it to RunAllCustomReports will load all reporters found that are valid for the given simulation type. The file must be in JSON format.	{ "Custom_Reports_Filename": "custom_reports.json" }
Incubation_Period_Log_Mean	float	0	3.40E+38	6	The mean of log normal for the incubation period distribution. Incubation_Period_Distribution must be set to LOG_NORMAL_DURATION.	{ "Incubation_Period_Distribution": "LOG_NORMAL_DURATION", "Incubation_Period_Log_Mean": 5.758, "Incubation_Period_Log_Width": 0.27 }
Incubation_Period_Log_Width	float	0	3.40E+38	1	The log width of log normal for the incubation period distribution. Incubation_Period_Distribution must be set to LOG_NORMAL_DURATION.	{ "Incubation_Period_Distribution": "LOG_NORMAL_DURATION", "Incubation_Period_Log_Mean": 5.758, "Incubation_Period_Log_Width": 0.27 }
Infectivity_Exponential_Baseline	float	0	1	0	The scale factor applied to Base_Infectivity at the beginning of a simulation, before the infectivity begins to grow exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.	{ "Infectivity_Exponential_Baseline": 0.1, "Infectivity_Exponential_Delay": 90, "Infectivity_Exponential_Rate": 45, "Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME" }
Infectivity_Exponential_Delay	float	0	3.40E+38	0	The number of days before infectivity begins to ramp up exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.	{ "Infectivity_Exponential_Baseline": 0.1, "Infectivity_Exponential_Delay": 90, "Infectivity_Exponential_Rate": 45, "Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME" }
Infectivity_Exponential_Rate	float	0	3.40E+38	0	The daily rate of exponential growth to approach to full infectivity after the delay set by Infectivity_Exponential_Delay has passed. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.	{ "Infectivity_Exponential_Rate": 45 }
Memory_Usage_Halting_Threshold_Working_Set_MB	integer	0	1.00E+06	8000	The maximum size (in MB) of working set memory before the system throws an exception and halts.	{ "Memory_Usage_Halting_Threshold_Working_Set_MB": 4000 }
Memory_Usage_Warning_Threshold_Working_Set_MB	integer	0	1.00E+06	7000	The maximum size (in MB) of working set memory before memory usage statistics are written to the log regardless of log level.	{ "Memory_Usage_Warning_Threshold_Working_Set_MB": 3500 }
Serialization_Time_Steps	array of integers	0	2.15E+09		The list of time steps after which to save the serialized state. 0 (zero) indicates the initial state before simulation, n indicates after the nth time step. By default, no serialized state is saved.	{ "Serialization_Time_Steps": [ 0, 10 ] }
Serialized_Population_Filenames	array of strings	NA	NA	NA	Array of filenames with serialized population data. The number of filenames must match the number of cores used for the simulation. The file must be in .dtk format.	{ "Serialized_Population_Filenames": [ "state-00010.dtk" ] }
Serialized_Population_Path	string	NA	NA	.	The root path for the serialized population files.	{ "Serialized_Population_Path": "../00_Generic_Version_1_save/output" }

To view all available configuration parameters, see Configuration parameters.

New demographics parameters ¶

In all simulation types, you can now assign properties like risk or quality of care to nodes using NodeProperties, which are configured much like IndividualProperties. In addition, a new property type is available for both nodes and individuals called InterventionStatus, which is used by the campaign file to distribute interventions based on the other interventions an individual or node has received. This property type was previously available only for individuals in the HIV simulation type and was known as the CascadeState. The relevant campaign parameters are described in the next section.

For more information, see the table below.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

NodeProperties

array of objects

NA

An array that contains parameters that add properties to nodes in a simulation. For example, you can define values for intervention status, risk, and other properties and assign values to different nodes.

{
    "NodeProperties": [{
        "Property": "Risk",
        "Values": ["HIGH", "MEDIUM", "LOW"],
        "Initial_Distribution": [0.1, 0.4, 0.5]
    }]
}

Property

enum

NA

The individual or node property type for which you will assign arbitrary values to create groups. You can then move individuals or nodes into or out of different groups or target interventions to particular groups. Possible values are:

Age_Bin: Assign individuals to age bins. Use with Age_Bin_Edges_In_Years. Cannot be used with NodeProperties.
Accessibility: Tag individuals or nodes based on their accessibility.
Geographic: Tag individuals or nodes based on geographic characteristics.
HasActiveTB: Tag individuals or nodes based on active TB status. Typically used only with HIV ART staging interventions.
InterventionStatus: Tag individuals or nodes based on intervention status, so that receiving an intervention can affect how other interventions are distributed. Use with Disqualifying_Properties and New_Property_Value in the campaign file.
Place: Tag individuals or nodes based on place.
Risk: Tag individuals or nodes based on disease risk.
QualityofCare: Tag individuals or nodes based on the quality of medical care.

{
    "Defaults": {
        "IndividualProperties": [{
            "Property": "Age_Bin",
            "Age_Bin_Edges_In_Years": [ 0, 6, 10, 20, -1 ]
        }]
    }
}

{
    "NodeProperties": [{
        "Property": "InterventionStatus",
        "Values": ["NONE", "RECENT_SPRAY"],
        "Initial_Distribution": [1.0, 0.0]
    }]
}

New campaign parameters ¶

The addition of NodeProperties in the demographics file also necessitated the addition of Node_Property_Restrictions to control how interventions are distributed based on the property values assigned to each node.

The new campaign parameters Disqualifying_Properties and New_Property_Value were added to every intervention to control how interventions are distributed based on properties assigned to individuals or nodes. Disqualifying_Properties prevents an intervention from being distributed to individuals or nodes with certain property values. New_Property_Value updates the property value after they receive an intervention.

These are generally used with the the property type InterventionStatus to control how interventions are distributed based on the interventions already received. For example, a household may be ineligible for clinical treatment for a length of time if they already received treatment during a drug campaign. This functionality was previously only available for individuals in the HIV simulation type and used parameters previously called Abort_States and Valid_Cascade_States.

The following event coordinators and intervention classes are new for this simulation type.

CommunityHealthWorkerEventCoordinator ¶

The CommunityHealthWorkerEventCoordinator coordinator class is used to model a health care worker’s ability to distribute interventions to the nodes and individual in their coverage area. This coordinator distributes a limited number of interventions per day, and can create a backlog of individuals or nodes requiring the intervention. For example, individual-level interventions could be distribution of drugs and node-level interventions could be spraying houses with insecticide.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Amount_In_Shipment	integer	1	2.15E+0	2.15E+09	The number of interventions (such as vaccine doses) that a health worker or clinic receives in a shipment. Interventions can only be distributed if they are in stock; stock is updated every Days_Between_Shipments with the Amount_In_Shipment.	{ "Amount_In_Shipment": 10 }
Days_Between_Shipments	float	1	3.40E+3	3.40E+38	The number of days to wait before a clinic or health worker receives a new shipment of interventions (such as vaccine doses). Interventions can only be distributed if they are in stock; stock is updated every Days_Between_Shipments with the Amount_In_Shipment.	{ "Days_Between_Shipments": 30 }
Demographic_Coverage	float	0	1	1	The fraction of individuals in the target demographic that will receive this intervention.	{ "Demographic_Coverage": 1 }
Duration	float	0	3.40E+3	3.40E+38	The number of days for an event coordinator to be active before it expires.	{ "Duration": 65 }
Initial_Amount	float	0	3.40E+3	6	The initial amount of stock of interventions (such as vaccine doses). Interventions can only be distributed if they are in stock; stock is updated every Days_Between_Shipments with the Amount_In_Shipment.	{ "Initial_Amount": 10 }
Initial_Amount_Distribution_Type	enum	NA	NA	NOT_INITIALIZED	The distribution type to set when initializing Initial_Amount. Possible values are: NOT_INITIALIZED No distribution set. FIXED_DURATION A constant duration. UNIFORM_DURATION A uniform random draw for the duration. GAUSSIAN_DURATION Duration of the active period is defined by the mean and standard deviation of the Gaussian distribution. Negative values are truncated at zero. EXPONENTIAL_DURATION The active period is the mean of the exponential random draw. POISSON_DURATION The active period is the mean of the random Poisson draw. LOG_NORMAL_DURATION The active period is a log normal distribution defined by a mean and log width. BIMODAL_DURATION The distribution is bimodal, the duration is a fraction of the active period for a specified period of time and equal to the active period otherwise. PIECEWISE_CONSTANT The distribution is specified with a list of years and a matching list of values. The duration at a given year is that specified for the nearest previous year. PIECEWISE_LINEAR The distribution is specified with a list of years and matching list of values. The duration at a given year is a linear interpolation of the specified values. WEIBULL_DURATION The duration is a Weibull distribution with a given scale and shape. DUAL_TIMESCALE_DURATION The duration is two exponential distributions with given means.	{ "Initial_Amount_Distribution_Type": "FIXED_DURATION" }
Initial_Amount_Max	float	0	3.40E+3	0	The maximum amount of initial stock when Initial_Amount_Distribution_Type is set to UNIFORM_DISTRIBUTION.	{ "Initial_Amount_Distribution_Type": "UNIFORM_DURATION", "Initial_Amount_Min": 5, "Initial_Amount_Max": 10 }
Initial_Amount_Mean	float	0	3.40E+3	6	The average amount of initial stock when Initial_Amount_Distribution_Type is set to GAUSSIAN_DISTRIBUTION.	{ "Initial_Amount_Distribution_Type": "GAUSSIAN_DISTRIBUTION", "Initial_Amount_Std_Dev": 1, "Initial_Amount_Mean": 5 }
Initial_Amount_Min	float	0	3.40E+3	0	The minimum amount of initial stock when Initial_Amount_Distribution_Type is set to UNIFORM_DISTRIBUTION.	{ "Initial_Amount_Distribution_Type": "UNIFORM_DURATION", "Initial_Amount_Min": 5, "Initial_Amount_Max": 10 }
Initial_Amount_Std_Dev	float	0	3.40E+3	1	The standard deviation for the amount of initial stock when Initial_Amount_Distribution_Type is set to GAUSSIAN_DISTRIBUTION.	{ "Initial_Amount_Distribution_Type": "GAUSSIAN_DISTRIBUTION", "Initial_Amount_Std_Dev": 1, "Initial_Amount_Mean": 5 }
Intervention_Config	JSON object	NA	NA	NA	The nested JSON of the actual intervention to be distributed by this event coordinator.	{ "Intervention_Config": { "class": "BroadcastEvent", "Broadcast_Event": "EventFromIntervention" } }
Max_Distributed_Per_Day	integer	1	2.15E+0	2.15E+09	The maximum number of interventions (such as vaccine doses) that can be distributed by health workers or clinics in a given day.	{ "Max_Distributed_Per_Day": 1 }
Max_Stock	integer	0	2.15E+0	2.15E+09	The maximum number of interventions (such as vaccine doses) that can be stored by a health worker or clinic. If the amount of interventions in a new shipment and current stock exceeds this value, only the number of interventions specified by this value will be stored.	{ "Max_Stock": 12 }
Node_Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. You can specify AND and OR combinations of key:value pairs with this parameter.	The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk. { "Node_Property_Restrictions": [{ "Place": "URBAN", "Risk": "MED" }, { "Place": "RURAL", "Risk": "LOW" } ] }
Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.	{ "Property_Restrictions": [ "Risk:HIGH" ] }
Property_Restrictions_Within_Node	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.	The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk. { "Property_Restrictions_Within_Node": [ {"Risk": "HIGH", "Geographic": "URBAN"}, {"Risk": "MEDIUM", "Geographic": "URBAN"} ] }
Target_Age_Min	float	0	3.40E+3	0	The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Demographic	enum	NA	NA	Everyone	The target demographic group. Possible values are: Everyone ExplicitAgeRanges ExplicitAgeRangesAndGender ExplicitGender PossibleMothers ArrivingAirTravellers DepartingAirTravellers ArrivingRoadTravellers DepartingRoadTravellers AllArrivingTravellers AllDepartingTravellers ExplicitDiseaseState	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Gender	enum	NA	NA	All	Specifies the gender restriction for the intervention. Possible values are: Male Female All	{ "Target_Gender": "Male" }
Target_Residents_Only	boolean	NA	NA	0	When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).	{ "Target_Residents_Only": 1 }
Trigger_Condition_List	array of strings	NA	NA	NoTrigger	The list of events that are of interest to the community health worker (CHW). If one of these events occurs, the individual or node is put into a queue to receive the CHW’s intervention. The CHW processes the queue when the event coordinator is updated. See Event list for possible values.	{ "Trigger_Condition_List": ["ListenForEvent"] }
Waiting_Period	float	0	3.40E+3	3.40E+38	The number of days a person or node can be in the queue waiting to get the intervention from the community health worker (CHW). If a person or node is in the queue, they will not be re-added to the queue if the event that would add them to the queue occurs. This allows them to maintain their priority, however they could be removed from the queue due to this waiting period.	{ "Waiting_Period": 15 }

ImportPressure ¶

The ImportPressure intervention class extends the ImportCases outbreak event. Rather than importing a deterministic number of cases on a scheduled day, ImportPressure applies a set of per-day rates of importation of infected individuals, over a corresponding set of durations. ImportPressure inherits from Outbreak; the Antigen and Genome parameters are defined as they are for all Outbreak events.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Antigen	integer	0	10	0	The antigenic ID of the outbreak infection.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease", "class": "OutbreakIndividual" } }
Daily_Import_Pressures	array of floats	0	3.40E+3	0	The rate of per-day importation for each node that the intervention is distributed to.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Durations": [100, 100, 100, 100, 100, 100, 100], "Daily_Import_Pressures": [0.1, 5.0, 0.2, 1.0, 2.0, 0.0, 10.0], "class": "ImportPressure" } }
Durations	array of integers	0	2.15E+0	1	The durations over which to apply import pressure.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Durations": [100, 100, 100, 100, 100, 100, 100], "Daily_Import_Pressures": [0.1, 5.0, 0.2, 1.0, 2.0, 0.0, 10.0], "class": "ImportPressure" } }
Genome	integer	-2.15E+0	2.15E+0	0	The genetic ID of the outbreak infection.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease", "class": "OutbreakIndividual", "Incubation_Period_Override": 0 } }
Import_Age	float	0	43800	365	The age (in days) of infected import cases.	{ "Import_Age": 10000 }
Incubation_Period_Override	integer	-1	2.15E+0	-1	The incubation period, in days, that infected individuals will go through before becoming infectious. This value overrides the incubation period set in the configuration file. Set to -1 to honor the configuration parameter settings.	{ "Incubation_Period_Override": 0 }
Number_Cases_Per_Node	integer	0	2.15E+0	1	The number of new cases of Outbreak to import (per node). Note This will increase the population and there is no control over demographics of these individuals.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Outbreak_Source": "ImportCases", "Number_Cases_Per_Node": 10, "class": "Outbreak" } }

IndividualImmunityChanger ¶

The IndividualImmunityChanger intervention class acts essentially as a MultiEffectVaccine, with the exception of how the behavior is implemented. Rather than attaching a persistent vaccine intervention object to an individual’s intervention list (as a campaign-individual-multieffectboostervaccine does), the IndividualImmunityChanger directly alters the immune modifiers of the individual’s susceptibility object and is then immediately disposed of. Any immune waning is not governed by Waning effect classes, as MultiEffectVaccine is, but rather by the immunity waning parameters in the configuration file.

Parameter	Data type	Maximum	Default	Description	Example
Boost_Acquire	float	1	0	Specifies the boosting effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.	{ "Boost_Acquire": 0.7 }
Boost_Mortality	float	1	0	Specifies the boosting effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.	{ "Boost_Mortality": 1.0 }
Boost_Threshold_Acquire	float	1	0	Specifies how much acquisition immunity is required before the vaccine changes from a prime to a boost.	{ "Boost_Threshold_Acquire": 0.0 }
Boost_Threshold_Mortality	float	1	0	Specifies how much mortality immunity is required before the vaccine changes from a prime to a boost.	{ "Boost_Threshold_Mortality": 0.0 }
Boost_Threshold_Transmit	float	1	0	Specifies how much transmission immunity is required before the vaccine changes from a prime to a boost.	{ "Boost_Threshold_Transmit": 0.0 }
Boost_Transmit	float	1	0	Specifies the boosting effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.	{ "Boost_Transmit": 0.5 }
Cost_To_Consumer	float	999999	10	The unit cost per vaccine (unamortized).	{ "Cost_To_Consumer": 10.0 }
Prime_Acquire	float	1	0	Specifies the priming effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.	{ "Prime_Acquire": 0.1 }
Prime_Mortality	float	1	0	Specifies the priming effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.	{ "Prime_Mortality": 0.3 }
Prime_Transmit	float	1	0	Specifies the priming effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.	{ "Prime_Transmit": 0.2 }

MultiEffectBoosterVaccine ¶

The MultiEffectBoosterVaccine intervention class is derived from MultiEffectVaccine and preserves many of the same parameters. Upon distribution and successful take, the vaccine’s effect in each immunity compartment (acquisition, transmission, and mortality) is determined by the recipient’s immune state. If the recipient’s immunity modifier in the corresponding compartment is above a user-specified threshold, then the vaccine’s initial effect will be equal to the corresponding priming parameter. If the recipient’s immune modifier is below this threshold, then the vaccine’s initial effect will be equal to the corresponding boost parameter. After distribution, the effect wanes, just like a MultiEffectVaccine. The behavior is intended to mimic biological priming and boosting.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Acquire_Config	JSON object	NA	NA	NA	The configuration for multi-effect vaccine acquisition. Specify how this effect decays over time using one of the Waning effect classes.	{ "Acquire_Config": { "Initial_Effect": 0.9, "Decay_Time_Constant": 2525, "class": "WaningEffectExponential" } }
Boost_Acquire	float	0	1	0	Specifies the boosting effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.	{ "Boost_Acquire": 0.7 }
Boost_Mortality	float	0	1	0	Specifies the boosting effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.	{ "Boost_Mortality": 1.0 }
Boost_Threshold_Acquire	float	0	1	0	Specifies how much acquisition immunity is required before the vaccine changes from a prime to a boost.	{ "Boost_Threshold_Acquire": 0.0 }
Boost_Threshold_Mortality	float	0	1	0	Specifies how much mortality immunity is required before the vaccine changes from a prime to a boost.	{ "Boost_Threshold_Mortality": 0.0 }
Boost_Threshold_Transmit	float	0	1	0	Specifies how much transmission immunity is required before the vaccine changes from a prime to a boost.	{ "Boost_Threshold_Transmit": 0.0 }
Boost_Transmit	float	0	1	0	Specifies the boosting effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.	{ "Boost_Transmit": 0.5 }
Cost_To_Consumer	float	0	999999	10	The unit cost per vaccine (unamortized).	{ "Cost_To_Consumer": 10.0 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
Mortality_Config	JSON object	NA	NA	NA	The configuration for multi-effect vaccine mortality. Specify how this effect decays over time using one of the Waning effect classes.	{ "Mortality_Config": { "Initial_Effect": 1.0, "Decay_Time_Constant": 2525, "class": "WaningEffectExponential" } }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Prime_Acquire	float	0	1	0	Specifies the priming effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.	{ "Prime_Acquire": 0.1 }
Prime_Mortality	float	0	1	0	Specifies the priming effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.	{ "Prime_Mortality": 0.3 }
Prime_Transmit	float	0	1	0	Specifies the priming effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.	{ "Prime_Transmit": 0.2 }
Transmit_Config	JSON object	NA	NA	NA	The configuration for multi-effect vaccine transmission. Specify how this effect decays over time using one of the Waning effect classes.	{ "Transmit_Config": { "Initial_Effect": 0.6, "Decay_Time_Constant": 2525, "class": "WaningEffectExponential" } }
Vaccine_Take	float	0	1	1	The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }

NodePropertyValueChanger ¶

The NodePropertyValueChanger intervention class sets a given node property to a new value. You can also define a duration in days before the node property reverts back to its original value, the probability that a node will change its node property to the target value, and the number of days over which nodes will attempt to change their individual properties to the target value. This node-level intervention functions in a similar manner as the individual-level intervention, PropertyValueChanger.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Daily_Probability	float	0	1	1	The probability that an individual will move to the Target_Property_Value.	{ "Intervention_Config": { "class": "PropertyValueChanger", "Disqualifying_Properties": [], "New_Property_Value": "", "Target_Property_Key": "Risk", "Target_Property_Value": "LOW", "Daily_Probability": 1.0, "Maximum_Duration": 0, "Revert": 0 } }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
Maximum_Duration	float	-1	3.40E+3	3.40E+38	The maximum amount of time individuals have to move to a new group. This timing works in conjunction with Daily_Probability.	{ "Intervention_Config": { "class": "PropertyValueChanger", "Disqualifying_Properties": [], "New_Property_Value": "", "Target_Property_Key": "Risk", "Target_Property_Value": "LOW", "Daily_Probability": 1.0, "Maximum_Duration": 0, "Revert": 0 } }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Revert	float	0	10000	0	The number of days before an individual moves back to their original group.	{ "Intervention_Config": { "class": "PropertyValueChanger", "Disqualifying_Properties": [], "New_Property_Value": "", "Target_Property_Key": "Risk", "Target_Property_Value": "LOW", "Daily_Probability": 1.0, "Maximum_Duration": 0, "Revert": 0 } }
Target_NP_Key_Value	string	NA	NA	NA	The NodeProperty key:value pair, as defined in the demographics file, to assign to the node.	{ "Target_NP_Key_Value": "InterventionStatus:NONE" }

SimpleBoosterVaccine ¶

The SimpleBoosterVaccine intervention class is derived from SimpleVaccine and preserves many of the same parameters. The behavior is much like SimpleVaccine, except that upon distribution and successful take, the vaccine’s effect is determined by the recipient’s immune state. If the recipient’s immunity modifier in the corresponding channel (acquisition, transmission, or mortality) is above a user-specified threshold, then the vaccine’s initial effect will be equal to the corresponding priming parameter. If the recipient’s immune modifier is below this threshold, then the vaccine’s initial effect will be equal to the corresponding boosting parameter. After distribution, the effect wanes, just like SimpleVaccine. In essence, this intervention provides a SimpleVaccine intervention with one effect to all naive (below- threshold) individuals, and another effect to all primed (above-threshold) individuals; this behavior is intended to mimic biological priming and boosting.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Boost_Effect	float	0	1	1	Specifies the boosting effect on [acquisition/transmission/mortality] immunity for previously exposed individuals (either natural or vaccine-derived). This does not replace current immunity, it builds multiplicatively on top of it.	{ "Intervention_Config": { "Cost_To_Consumer": 10.0, "Vaccine_Take": 1, "Vaccine_Type": "MortalityBlocking", "Prime_Effect": 0.25, "Boost_Effect": 0.45, "Boost_Threshold": 0.0, "Waning_Config": { "Box_Duration": 10, "Initial_Effect": 1, "class": "WaningEffectBox" }, "class": "SimpleBoosterVaccine" } }
Boost_Threshold	float	0	1	0	Specifies how much immunity is required before the vaccine changes from a priming effect to a boosting effect.	{ "Intervention_Config": { "Cost_To_Consumer": 10.0, "Vaccine_Take": 1, "Vaccine_Type": "MortalityBlocking", "Prime_Effect": 0.25, "Boost_Effect": 0.45, "Boost_Threshold": 0.0, "Waning_Config": { "Box_Duration": 10, "Initial_Effect": 1, "class": "WaningEffectBox" }, "class": "SimpleBoosterVaccine" } }
Cost_To_Consumer	float	0	999999	10	The unit cost per vaccine (unamortized).	{ "Cost_To_Consumer": 10.0 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Efficacy_Is_Multiplicative	boolean	NA	NA	1	The overall vaccine efficacy when individuals receive more than one vaccine. When set to true (1), the vaccine efficacies are multiplied together; when set to false (0), the efficacies are additive.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Prime_Effect	float	0	1	1	Specifies the priming effect on [acquisition/transmission/mortality] immunity for naive individuals (without natural or vaccine-derived immunity).	{ "Intervention_Config": { "Cost_To_Consumer": 10.0, "Vaccine_Take": 1, "Vaccine_Type": "MortalityBlocking", "Prime_Effect": 0.25, "Boost_Effect": 0.45, "Boost_Threshold": 0.0, "Waning_Config": { "Box_Duration": 10, "Initial_Effect": 1, "class": "WaningEffectBox" }, "class": "SimpleBoosterVaccine" } }
Vaccine_Take	float	0	1	1	The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Vaccine_Type	enum	NA	NA	Generic	The type of vaccine to distribute in a vaccine intervention. Possible values are: Generic The vaccine can reduce transmission, acquisition, and mortality. TransmissionBlocking The vaccine will reduce pathogen transmission. AcquisitionBlocking The vaccine will reduce the acquisition of the pathogen by reducing the force of infection experienced by the vaccinated individual. MortalityBlocking The vaccine reduces the disease-mortality rate of a vaccinated individual.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Waning_Config	JSON object	NA	NA	NA	The configuration of how the intervention efficacy wanes over time. Specify how this effect decays over time using one of the Waning effect classes.	{ "Waning_Config": { "Box_Duration": 3650, "Initial_Effect": 1, "class": "WaningEffectBox" } }

EMOD v2.10 ¶

The EMOD v2.10 release includes new and updated malaria configuration, demographic, and intervention parameters. With this release, EMOD now uses Visual Studio 2015, Boost 1.61.0, and SCons 2.5.0.

EMOD software upgrades ¶

Microsoft Visual Studio: EMOD now uses Visual Studio 2015, and Visual Studio 2012 is no longer supported. The Visual Studio solution file in the EMOD source, EradicationKernel, has been updated for Visual Studio 2015. If you have custom reporter EMODules (DLLS) that were built using Visual Studio 2012, you will need to rebuild them with Visual Studio 2015; otherwise, your simulation will crash when it attempts to load the DLLs built by Visual Studio 2012.
Boost: EMOD now supports using Boost 1.61.0. If you continue to use Boost 1.51.0, you will get the following warning, “Unknown compiler version - please run the configure tests and report the results.”
Environment variables: To make it easier to use Boost and Python with Visual Studio, IDM paths have been created. These two paths, IDM_BOOST_PATH and IDM_PYTHON_PATH, need to be added to your environment variables by using either the setx command from a command line or the Windows System Properties panel.
SCons: EMOD was tested using SCons 2.5.0, as it supports Visual Studio 2015. If you do not add the new IDM environment variables for Boost and Python, you will need to modify the Boost and Python paths in the SConstruct file in the EMOD root directory.
Python: EMOD was tested with Python 2.7.11 and 2.7.12. If you are building the EMOD executable (Eradication.exe) and have an earlier version of Python (for example, 2.7.2), you will see the following warning message on some files, “c:python27includepymath.h(22): warning C4273: ‘round’: inconsistent dll linkage.” Upgrade to Python 2.7.11 or 2.7.12 to get rid of the warning message.

For more information, see Prerequisites for working with EMOD source code.

EMOD installation¶

Epidemiological MODeling software (EMOD) can be installed on computers running Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit) or CentOS 7.1 on Azure. For both operating systems, there are relatively few prerequisites required and the EMOD executable (Eradication.exe) or Eradication binary is pre- built from the latest EMOD source code release. After completing installation, you can run simulations locally or on an HPC cluster using real-world data.

Note

If you want to download and modify the EMOD source code and build the Eradication.exe or Eradication binary yourself, see EMOD source code installation.

Install EMOD on Windows¶

Follow the steps below to install EMOD on computers running Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit). This installs the pre-built Eradication.exe and all software necessary to run simulations.

Note

If you want to download and modify the EMOD source code and build the Eradication.exe yourself, see EMOD source code installation.

The EMOD executable (Eradication.exe) is tested using Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit). Windows HPC Server is used for testing remote simulations on a high-performance computing (HPC) cluster and the other Windows operating systems are used to test local simulations.

Install the Microsoft HPC Pack 2012 Client Utilities Redistributable Package (64-bit). See http://www.microsoft.com/en-us/download/details.aspx?id=36044 for instructions.
Install the Microsoft MPI v8. See https://www.microsoft.com/en-us/download/details.aspx?id=54607 for instructions, being sure to run the MSMpiSetup.exe file.
Download the EMOD executable (Eradication.exe). See EMOD releases on GitHub.

(Optional) Install plotting software¶

None of the following plotting software is required to run simulations with EMOD, but they are useful for creating graphs from and evaluating the model output. In addition, EMOD provides many Python scripts for analyzing data.

Note

IDM does not provide support or guarantees for any third-party software, even software that we recommend you install. Send feedback if you encounter any issues, but any support must come from the makers of those software packages and their user communities.

Python and Python packages¶

Python is required to install many of the software packages described below.

We recommended that you download some of the Python packages from http://www.lfd.uci.edu/~gohlke/pythonlibs, a page compiled by Christoph Gohlke, University of California, Irvine. The libraries there are compiled Windows binaries, including the 64-bit versions required by EMOD.

Install Python 2.7.11 or 2.7.12. See https://www.python.org/downloads/ for instructions.
- In the Customize Python dialog box, verify that Add python.exe to PATH is selected to add Python to the PATH environment variable on your computer.
From Control Panel, select Advanced system settings, and then click Environment Variables.
Add a new variable called IDM_PYTHON_PATH and set it to the directory where you installed Python, and then click OK.
Open a Command Prompt window and type the following to verify installation:
```
python --version
```

The Python package manager, pip, is installed as part of Python 2.7.11 or 2.7.12 and is used to install other software packages.

Python utilities¶

The Python utilities dateutil, six, and pyparsing provide text parsing and datetime functionality.

Open a Command Prompt window.

Enter the following commands:

pip install python-dateutil
pip install pyparsing

NumPy¶

The NumPy package adds support for large, multi-dimensional arrays and matrices to Python.

Go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy and select the WHL file for NumPy 1.11.3 (64-bit) that is compatible with Python 2.7.11 or 2.7.12.
Save the file to your Python installation directory.
Open a Command Prompt window and navigate to the Python installation directory, then enter the following, substituting the name of the specific NumPy WHL file you downloaded:
```
pip install numpy-1.x.x+mkl-cp27m-win_amd64.whl
```

Matplotlib¶

Matplotlib is a Python library for making publication quality plots using syntax familiar to MATLAB users. Matplotlib also uses NumpPy for numeric manipulation. Output formats include PDF, Postscript, SVG, and PNG, as well as a screen display.

Go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#matplotlib and select the WHL file for Matplotlib 1.5.3 (64-bit) that is compatible with Python 2.7.11 or 2.7.12.
Save the file to your Python installation directory.
Open a Command Prompt window and navigate to the Python installation directory, then enter the following, substituting the name of the specific matplotlib WHL file you downloaded:
```
pip install matplotlib-1.x.x+mkl-cp27m-win_amd64.whl
```

R¶

R is a free software environment for statistical computing and graphics.

Go to https://www.r-project.org/ and install R 3.2.0 (64-bit).

MATLAB¶

MATLAB is a high-level language and interactive environment for numerical computation, visualization, and programming. The MATLAB Statistics and Machine Learning Toolbox™ provides functions and applications to describe, analyze and model data using statistics and machine learning algorithms.

Go to http://www.mathworks.com/products/matlab/ and install MATLAB R2015a.
If desired, go to https://www.mathworks.com/products/statistics.html and install the MATLAB Statistics and Machine Learning Toolbox™ R2015a.

Install EMOD on CentOS on Azure¶

Follow the steps below to install EMOD on CentOS 7.1 on Azure environments. This installs the pre- built Eradication binary and all software needed to run simulations. The setup script installs most prerequisite software, including Python and the Python packages dateutil, six, pyparsing, NumPy, and matplotlib. Other prerequisites, such as Boost 1.61.0 and Microsoft MPI v8, are declared by the script as required. Because the installation instructions will vary based on the particular distribution you are running, instruction information is not included here.

Note

If you want to download and modify the EMOD source code and build the Eradication binary yourself, see EMOD source code installation.

The Eradication binary is tested and supported on a CentOS 7.1 on Azure virtual machine. It has also been successfully built and run on Ubuntu, SUSE, and Arch, but has not been tested and is not supported on those Linux distributions.

Note

IDM does not provide support or guarantees for any third-party software, even software that we recommend you install. Send feedback if you encounter any issues, but any support must come from the makers of those software packages and their user communities.

The script provides the option of installing the EMOD source code and input data files provided by IDM, but these steps will install a pre-built version of the Eradication binary. For information on building the Eradication binary from source code, see EMOD source code installation.

Before you begin, you must have the following:

sudo privileges to install packages
15 GB free in your home directory (if you install the EMOD source code and input data files)
An Internet connection

Download and run the PrepareLinuxEnvironment.sh script on EMOD releases on GitHub.

Respond to the prompts for information while the script is running. If you choose not to download the EMOD source and input data files, do the following. This example assumes that a directory named IDM is in your home directory and contains the subdirectories EMOD, containing the EMOD source code, and EMOD_InputData, containing the input data files directory.
1. Set the EMOD_ROOT environment variable to the path to the EMOD source path:
```
EMOD_ROOT=~/IDM/EMOD
```
2. Put Scripts and . in the path:
```
export PATH=$PATH:.:$EMOD_ROOT/Scripts
```
3. Create a symlink from the EMOD directory to InputDataFiles:
```
ln -s /home/general/IDM/EMOD-InputData $EMOD_ROOT/InputData
```
4. If you run simulations using in the same session that you updated EMOD_ROOT and the Scripts path, reload the .bashrc file using source .bashrc.
Download the Eradication binary for CentOS 7.1 on Azure. See on EMOD releases on GitHub.

(Optional) Install plotting software¶

None of the following plotting software is required to run simulations with EMOD, but they are useful for creating graphs from and evaluating the model output. In addition, EMOD provides many Python scripts for analyzing data.

Note

IDM does not provide support or guarantees for any third-party software, even software that we recommend you install. Send feedback if you encounter any issues, but any support must come from the makers of those software packages and their user communities.

R¶

R is a free software environment for statistical computing and graphics.

Go to https://www.r-project.org/ and install R 3.2.0 (64-bit).

MATLAB¶

MATLAB is a high-level language and interactive environment for numerical computation, visualization, and programming. The MATLAB Statistics and Machine Learning Toolbox™ provides functions and applications to describe, analyze and model data using statistics and machine learning algorithms.

Go to http://www.mathworks.com/products/matlab/ and install MATLAB R2015a.
If desired, go to https://www.mathworks.com/products/statistics.html and install the MATLAB Statistics and Machine Learning Toolbox™ R2015a.

Introduction to the software¶

The Institute for Disease Modeling (IDM) develops detailed simulations of disease transmission through the use of extensive and complex software modeling. The primary software, Epidemiological MODeling software (EMOD), helps determine the combination of health policies and intervention strategies that can lead to disease eradication. EMOD calculates how diseases may spread in particular areas and is used to analyze the effects of current and future health policies and intervention strategies. It supports infectious disease campaign planning, data gathering, new product development, and policy decisions. We share this modeling software with the research community to advance the understanding of disease dynamics.

EMOD supports the following simulation types for modeling a variety of diseases:

Generic disease (GENERIC_SIM)
Vector-borne diseases (VECTOR_SIM)
Malaria (MALARIA_SIM)
Tuberculosis (TB_SIM)
Sexually transmitted infections (STI_SIM)
HIV (HIV_SIM)

The illustration below shows how the simulation types are built upon one another. All parameters available in the generic simulation type are inherited by the vector simulation type. The vector simulation type adds additional parameters specific to the biology of vector-borne diseases, which in turn are inherited by the malaria simulation type and so on. Therefore, depending on the simulation type you select, different parameters are available for you to use. In addition, simulation types for broader classes of disease can be extended to build your own disease-specific model.

Simulation type inheritance¶

EMOD is a stochastic, agent-based model that simulates the actions and interactions of individuals within geographic areas known as nodes to understand the disease dynamics in a population over time. EMOD can produce statistically significant results over a broad set of parameters and scenarios. Quantitative analysis of the simulated output enables disease eradication efforts to make more data-driven decisions. The IDM research team has published many articles related to modeling, as well as the modeling concepts underpinning EMOD. For a list of published articles, see IDM Publications.

This section provides the conceptual overview of EMOD. The architecture diagram below shows, at a high level, how the system functions. If you run simulations in parallel on a multi-node cluster, there is also a Message Passing Interface (MPI) component used to pass data between multiple instances of EMOD.

High-level EMOD system architecture¶

Input data files¶

Not all files accepted as input for running a simulation are considered input data files. Rather, input data files contain the relatively fixed information about a population to model. For example, climate, geography, demographics, and migration data.

Configuration file¶

The configuration file contains parameters that control many different aspects of the simulation. EMOD provides hundreds of parameters for you to configure your simulation. For example, configuration parameters can control the following:

The disease or disease class to simulate (simulation type)
The name and location for the input data files and output files
Whether to include births, deaths, and migration
Disease attributes, such as infectivity, transmission, immunity, and mortality
The computing resources to use

Campaign file¶

The campaign file contains parameters that distribute outbreaks and the interventions used to control the spread of disease. For example, campaign parameters can control the following:

Target demographic (age, location, gender, etc.) for interventions
Diagnostic tests to use
The cost and timing of interventions

Running a simulation¶

The EMOD executable (Eradication.exe) accepts the input data, configuration, and campaign files and then simulates the susceptibility and infection of individuals within each geographic node. The simulation type controls the transmission mechanism of the disease. After the simulation is complete, the Eradication.exe produces output reports that describe various aspects of the disease dynamics within the modeled population.

You can run simulations locally or on a multi-core cluster. Because the model is stochastic, you must run simulations multiple times to produce scientifically valid results.

Depending on how you run the simulation, other programs or environments external to EMOD may also output error and logging files.

Output files¶

After the simulation finishes, a reporter extracts simulation data, aggregates it, and outputs it to a file (known as an output report). Most of the reports are also JSON files, the most important of which is InsetChart.json. The InsetChart.json file provides simulation-wide averages of disease prevalence at each time step.

See the topics listed below for a more detailed explanation of each of these system components.

Input data files¶

EMOD accepts the following categories of input data files that contain the relatively fixed information about the population within each geographic node. For example, the number of individuals, geographic data, climate, demographics, and migration data. This is in contrast to the demographic, geographic, and migration parameters in the configuration file that control simulation-wide qualities, such as enabling air migration across all nodes in the simulation.

Although a demographics file is the only required input data file, additional files are generally needed for a realistic simulation. The demographics files use JavaScript Object Notation (JSON). The other input data files use both a JSON file for metadata and an associated binary file that contains the actual data.

The Institute for Disease Modeling (IDM) provides collections of input data files for many different locations in the world for download on GitHub. Except for the demographics file, you will typically use these input data files in their default state. See Use input data files for more information.

Demographics¶

Demographics files are JSON formatted files containing information on the demographics of the population in a geographical region to simulate. For example, the number of individuals and the distribution for age, gender, immunity, risk, and mortality.

In addition, demographics files are useful for creating heterogeneous groups within a population. You can define values for accessibility, age, geography, risk, and other properties and assign individuals to groups based on those property values. For example, you might want to divide a population up into different bins based on age so you can target interventions to individuals in a particular age range. Another common use is to configure treatment coverage to be higher for individuals in easily accessible regions and lower for individuals in areas that are difficult to access.

EMOD assumes homogeneous mixing and disease transmission for the generic simulation type. You can use the HINT feature to add heterogeneous transmission to your generic model. You cannot manually configure heterogeneous transmission using HINT with other simulation types because the heterogeneity in transmission for specific diseases and disease classes is already configured by the simulation type.

You can specify multiple demographics files, which function as a “base layer” file and one or more “overlay” files that override the base layer configuration. Overlay files can change the value of parameters already specified in the base layer or add new parameters. Support for multiple demographics layers allows for the following scenarios:

Separating different sets of parameters and values into individual layers (for example, to separate those that are useful for specific diseases into different layers)
Adding new parameters for a simulation into a new layer for easier prototyping
Overriding certain parameters of interest in a new layer
Overriding certain parameters for a particular sub-region
Simulating subsets of a larger region for which input data files have been constructed

Migration¶

Migration files describe the rate of migration of individuals in and out of a geographic node. There are four types of migration files that can be used by EMOD: local migration, regional migration, air migration, and sea migration.

For all types, migration data is contained in a set of two files, a JSON metadata file with header information and a binary data file with the actual migration data. Both files are required. The basic file structure is identical for all types, with the only exception being the number of columns per row allowed to each type.

Local¶

Local migration describes the foot travel of people into and out of adjacent nodes. A local migration file is required for simulations that support more than one node.

Regional¶

Regional migration describes migration via a road or rail network. If a node is not part of the network, the regional migration of individuals to and from that node considers the closest road hub city. When you create the migration file, you must create a Voronoi tiling based on road hubs of the region, with each non-hub connected to the hub of its tile.

Air¶

Air migration describes migration via airplane travel. It is usually required for simulations of an entire country or larger geographies.

Sea¶

Sea migration describes migration via ship.

Climate¶

There are two general types of climate files usable by EMOD: climate files generated through actual data, referred to as “climate by data,” and climate files generated from the Koppen classification system, referred to as “climate by Koppen.”

For both types, climate data is contained in a set of two files, a JSON metadata file with header information and a binary file that contains the actual climate data. Both files are required.

Climate by data¶

Climate by data files contain real data gathered from weather stations in the region to be simulated. This includes rainfall, temperature, relative humidity, and so on.

Climate by Koppen¶

Climate by Koppen files contain the Koppen classifier for the region. The Koppen classification system is one of the most widely used climate classification systems. It makes the assumption that native vegetation is the best reflection of climate.

Load balancing¶

When running a simulation on a multi-core HPC cluster, you can include a load balancing file to control how the computing load is distributed across the cluster. The load balancing file is a binary file that allocates the simulation of each geographic node to cores in the cluster. If no file is submitted, EMOD allocates nodes to cores according to a checkerboard pattern.

Configuration file¶

The primary means of configuring the disease simulation is the configuration file. This required file is a JavaScript Object Notation (JSON) formatted file that is typically named config.json. The configuration file controls many different aspects of the simulation. For example,

The names of the campaign file and input data files to use
Simulation-wide demographics, climate, and migration data
General disease attributes such as infectivity, immunity, mortality, and so on
Attributes specific to the disease type being modeled, including treatment efficacy
The reports to output from the simulation

IDM provides complete simulation scenarios in the Regression directory on GitHub. Within each of the simulation subdirectories, there is a config.json file. The Scenarios subdirectory contains the files used in the tutorials.

The following is an example of configuration file.

{
    "parameters": {
        "Acquisition_Blocking_Immunity_Decay_Rate": 0.1,
        "Acquisition_Blocking_Immunity_Duration_Before_Decay": 60,
        "Age_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE",
        "Animal_Reservoir_Type": "NO_ZOONOSIS",
        "Base_Incubation_Period": 0,
        "Base_Individual_Sample_Rate": 1,
        "Base_Infectious_Period": 4,
        "Base_Infectivity": 3.5,
        "Base_Mortality": 0,
        "Base_Population_Scale_Factor": 1,
        "Birth_Rate_Dependence": "POPULATION_DEP_RATE",
        "Birth_Rate_Time_Dependence": "NONE",
        "Burnin_Cache_Mode": "none",
        "Burnin_Cache_Period": 0,
        "Burnin_Name": "",
        "Campaign_Filename": "campaign.json",
        "Climate_Model": "CLIMATE_OFF",
        "Config_Name": "00_DEFAULT",
        "Custom_Reports_Filename": "NoCustomReports",
        "Death_Rate_Dependence": "NOT_INITIALIZED",
        "Default_Geography_Initial_Node_Population": 1000,
        "Default_Geography_Torus_Size": 10,
        "Demographics_Filenames": [
            "../00_Default/demographics.json"
        ],
        "Enable_Absolute_Time": "NO",
        "Enable_Aging": 1,
        "Enable_Birth": 1,
        "Enable_Default_Reporting": 1,
        "Enable_Default_Shedding_Function": 1,
        "Enable_Demographics_Birth": 0,
        "Enable_Demographics_Builtin": 0,
        "Enable_Demographics_Gender": 1,
        "Enable_Demographics_Risk": 0,
        "Enable_Demographics_Reporting": 1,
        "Enable_Disease_Mortality": 0,
        "Enable_Heterogeneous_Intranode_Transmission": 0,
        "Enable_Immune_Decay": 0,
        "Enable_Immunity": 1,
        "Enable_Interventions": 1,
        "Enable_Maternal_Infection_Transmission": 0,
        "Enable_Natural_Mortality": 0,
        "Enable_Property_Output": 0,
        "Enable_Spatial_Output": 0,
        "Enable_Superinfection": 0,
        "Enable_Vital_Dynamics": 0,
        "Geography": "",
        "Immunity_Acquisition_Factor": 0,
        "Immunity_Initialization_Distribution_Type": "DISTRIBUTION_OFF",
        "Immunity_Mortality_Factor": 0,
        "Immunity_Transmission_Factor": 0,
        "Incubation_Period_Distribution": "FIXED_DURATION",
        "Individual_Sampling_Type": "TRACK_ALL",
        "Infection_Updates_Per_Timestep": 1,
        "Infectious_Period_Distribution": "EXPONENTIAL_DURATION",
        "Infectivity_Scale_Type": "CONSTANT_INFECTIVITY",
        "Job_Node_Groups": "Chassis08",
        "Job_Priority": "BELOWNORMAL",
        "Listed_Events": [],
        "Load_Balance_Filename": "",
        "Local_Simulation": 0,
        "Maternal_Transmission_Probability": 0,
        "Max_Individual_Infections": 1,
        "Max_Node_Population_Samples": 40,
        "Migration_Model": "NO_MIGRATION",
        "Minimum_Adult_Age_Years": 15,
        "Mortality_Blocking_Immunity_Decay_Rate": 0.001,
        "Mortality_Blocking_Immunity_Duration_Before_Decay": 60,
        "Mortality_Time_Course": "DAILY_MORTALITY",
        "Node_Grid_Size": 0.042,
        "Num_Cores": 1,
        "Number_Basestrains": 1,
        "Number_Substrains": 1,
        "PKPD_Model": "FIXED_DURATION_CONSTANT_EFFECT",
        "Population_Density_C50": 30,
        "Population_Density_Infectivity_Correction": "CONSTANT_INFECTIVITY",
        "Population_Scale_Type": "USE_INPUT_FILE",
        "Report_Event_Recorder": 0,
        "Run_Number": 1,
        "Sample_Rate_0_18mo": 1,
        "Sample_Rate_10_14": 1,
        "Sample_Rate_15_19": 1,
        "Sample_Rate_18mo_4yr": 1,
        "Sample_Rate_20_Plus": 1,
        "Sample_Rate_5_9": 1,
        "Sample_Rate_Birth": 1,
        "Serialization_Test_Cycles": 0,
        "Simulation_Duration": 90,
        "Simulation_Timestep": 1,
        "Simulation_Type": "GENERIC_SIM",
        "Start_Time": 0,
        "Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY",
        "Transmission_Blocking_Immunity_Decay_Rate": 0.1,
        "Transmission_Blocking_Immunity_Duration_Before_Decay": 60,
        "x_Air_Migration": 1,
        "x_Birth": 1,
        "x_Local_Migration": 1,
        "x_Other_Mortality": 1,
        "x_Population_Immunity": 1,
        "x_Regional_Migration": 1,
        "x_Sea_Migration": 1,
        "x_Temporary_Larval_Habitat": 1
    }
}

Overlay file¶

As you work more with EMOD, you may want to change the values of a view parameters of interest while keeping the rest constant, for example, when running simulation experiments or testing source code changes.

You have the option of creating an overlay file to keep configuration parameters of interest in a separate file from the complete configuration file that contains default values. These files can be flattened into a single file and the values in the overlay file will override those in the default file.

See Configuration parameters for a comprehensive list and description of all parameters available to use in the configuration file for this simulation type.

Campaign file¶

The campaign file is an optional file that distributes outbreaks and contains all parameters that define the collection of interventions that make up a disease eradication campaign. For example,

When, how, and who to test for a disease
When, how, and who to distribute treatments to
The vaccines or other preventative interventions to use
The medications to use for treatment

Like the configuration file, the campaign file is a JavaScript Object Notation (JSON) formatted file. It is hierarchically organized into logical groups of parameters that can have arbitrary levels of nesting. For some interventions, there can be a very complex hierarchical structure, including recursion. Typically, the file is named campaign.json. The relative path to this file is specified by Campaign_Filename in the configuration file.

To distribute an intervention, you must configure the following:

campaign event: A JSON object that determines when and where an intervention is distributed during a campaign.
event coordinator: A JSON object that determines who will receive a particular intervention during a campaign.
intervention: A JSON object that determines what will be distributed to reduce the spread of a disease. An intervention can be distributed either to an individual (such as a vaccine, drug, or bednet) or to a node (such as a larvicide). Sometimes this can be an intermediate intervention that schedules another intervention.

IDM provides complete simulation scenarios in the Regression directory on GitHub. Within each of the simulation subdirectories, there is a campaign.json file. The Scenarios subdirectory contains the files used in the tutorials.

Overlay file¶

As you work more with EMOD, you may want to change the values of a view parameters of interest while keeping the rest constant, for example, when running simulation experiments or testing source code changes.

You have the option of creating an overlay file to keep campaign parameters of interest in a separate file from the complete campaign file that contains default values. These files can be flattened into a single file and the values in the overlay file will override those in the default file.

See Campaign parameters for a comprehensive list and description of all parameters available to use in the campaign file for this simulation type.

Running a simulation¶

The EMOD executable (Eradication.exe) consumes the input data files, configuration file, and, optionally, campaign file to run simulations that model disease dynamics and campaign efficacy. You have a few different options for running simulations. The option you choose will depend upon whether you want to run one or more simulations, to run simulations locally or on a remote cluster (for large simulations or multiple simulations), or to run simulations for debugging the source code. This topic briefly describes the different options you have for running simulations.

Run a single simulation¶

You will generally want to run simulations using the EMOD command-line options. This will run a single simulation and put the output files in a local directory.

You must either download the latest version of Eradication.exe from GitHub or clone the EMOD source from GitHub and build Eradication.exe yourself. This gives you access to the latest features and parameters for EMOD.

Run multiple simulations¶

Because the EMOD model is stochastic, simulations must be run multiple times to return scientifically valid results. Therefore, you have the following options to run multiple simulations at a time, either locally or remotely on a high-performance computing (HPC) cluster. Generally, only small simulations should be run locally.

Many of these options are scripting languages that you can also use to modify the files consumed by EMOD, simplifying your workflow when running many simulations.

Run simulations for debugging¶

If you are helping advance the EMOD model by contributing to source code, there are other options for running simulations that provide debugging support. These options for running simulations are not recommended if you are not modifying the source code.

You can run a simulation locally from Visual Studio using the built-in debugger. You will be able to put in breakpoints and step through the code while inspecting the values of different state variables throughout the simulation.

You can use regression_test.py in the GitHub Regression directory to run multiple simulations on a cluster, including running the suite of regression tests run by the IDM testing team.

Output files¶

After running a simulation, the simulation data is extracted, aggregated, and saved as an output report. Depending on your configuration, one or more output reports will be created, each of which summarize different data from the simulation. Output reports can be in JSON, CSV, or binary file formats.

The EMOD functionality that produces an output report is known as a reporter. EMOD provides several built-in reporters for outputting data from simulations. By default, EMOD will always generate the report InsetChart.json, which contains the simulation-wide average disease prevalence by time step. If none of the provided reports generates the output report that you require, you can create a custom reporter.

If you want to visualize the data output from an EMOD simulation, you must use graphing software to plot the output reports. In addition to output reports, EMOD will generate error and logging files to help troubleshoot any issues you may encounter.

Introduction to disease modeling¶

To understand the complex dynamics underlying disease transmission, epidemiologists utilize a set of models called compartmental models. Developed in the early 20th century, these models stratify a population into groups, generally based on their risk or infection status. Underlying these models is a system of differential equations, so the model is able to track the population as a function of time. Further, the models track the number of people in each category, are able to predict the various properties of pathogen spread, can estimate the duration of epidemics, and can be used to understand how different situations or interventions can impact the outcome of pathogen spread. To see a typical plot of a population in SIR conditions, see the plot in Types of compartmental models. If you would like a more in-depth introduction to epidemiology and disease modeling, you may want to take the Epidemics course from The Pennsylvania State University through Coursera.

An agent-based model (ABM) is another powerful tool used to help understand the complexity inherent in disease transmission systems. These models form a type of “microscale model,” where they simulate the simultaneous interactions of agents in an effort to re-crate complex phenomena. Each agent (such as a human, or vector) can be assigned a variety of “properties” (for example, age, gender, etc), and their behavior and interactions with one another are determined by using decision rules. These models have strong predictive power, and are able to leverage spatial and temporal dynamics. Further, complex environments can be developed in which the agents act, and agents may “learn” from interactions or “adapt” to their environment. As a result, ABMs are excellent for identifying “emerging properties” of the system: patterns that are not explicitly modeled, but instead occur as a consequence of the rules that govern the agents.

Agent-based models are extensively used in epidemiology due to their predictive power in modeling the spread (or conversely, control) of epidemics. A popular type of ABM for this is one in which each agent’s rules follow the dynamics specified in the compartmental models, where each agent flows through the compartments as a function of both “within-host” rules (such as duration of infection) and interactions between agents (such as becoming infected when coming into contact with an infectious agent). By combining the epidemiological basis of compartmental models with the flexibility of an agent-based model, this type of ABM is quite powerful due to their ability to simultaneously address the ecology, epidemiology, and pathology of complex systems.

Why use disease modeling¶

Disease models play an important role in understanding and managing the transmission dynamics of various pathogens. We can use them to describe the spatial and temporal patterns of disease prevalence, as well as to explore or better understand the factors that influence infection incidence. Modeling is a key step in understanding what treatments and interventions can be most effective, how cost-effective these approaches may be, and what specific factors need to be considered when trying to eradicate disease.

Types of compartmental models¶

The following diagrams illustrate common compartmental models.

SIR Plot, showing typical categorization of a population into Susceptible, Infectious, and Recovered states.¶

Definitions¶

Susceptible: Individual is able to become infected.
Exposed: Individual has been infected with a pathogen, but due to the pathogen’s incubation period, is not yet infectious.
Infectious: Individual is infected with a pathogen and is capable of transmitting the pathogen to others.
Recovered: Individual is either no longer infectious, or “removed” from the population.

SI (Susceptible - Infectious) model¶

In this situation, people never leave the infectious state and have life-long infections. For example, herpes is a disease with life-long infectiousness. The dashed line shows how the model becomes an SIS (Susceptible - Infectious - Susceptible) model, where infection does not confer immunity (or there is waning immunity). Individuals have repeat or reoccurring infections, and infected individuals return to the susceptible state. For example, sexually transmitted diseases such as gonorrhea or chlamydia fall into this group.

SI - SIS model¶

SIR (Susceptible - Infectious - Recovered) model¶

In this category, individuals in the recovered state gain immunity to the pathogen. For example, measles, mumps, rubella, and pertussis may be modeled using the SIR framework. The dashed line shows how the model becomes an SIRS (Susceptible - Infectious - Recovered - Susceptible) model, where recovery does not confer life-long immunity, and individuals may become susceptible again.

SIR - SIRS model¶

SEIR (Susceptible - Exposed - Infectious - Recovered) model¶

In this category, individuals experience a long incubation duration (the “exposed” category), such that the individual is infected but not yet infectious. For example chicken pox, and even vector-borne diseases such as Dengue Hemorrhagic fever have a long incubation duration where the individual cannot yet transmit the pathogen to others. The dashed line shows how the model becomes an SEIRS (Susceptible - Exposed - Infectious - Recovered - Susceptible) model, where recovered people may become susceptible again (recovery does not confer life-long immunity). For example, rotovirus and malaria are diseases with long incubation durations, and where infection only confers temporary immunity.

SEIR - SEIRS model¶

Fundamental concepts in epidemiology and disease modeling¶

Basic reproductive number (R₀)

The average number of secondary infections generated by the first infectious individual in a population of completely susceptible individuals. R₀=S*L* $\beta$ (where S = the number of susceptible hosts, L = length of infection, and $\beta$ = transmissibility). When R₀> 1, disease will spread. It is essentially a measure of the expected or average outcome of transmission. This is the threshold parameter used to determine whether or not an epidemic will occur, and determines:

The initial rate of increase of an epidemic (the exponential growth phase).
The final size of an epidemic (what fraction of susceptibles will be infected).
The endemic equilibrium fraction of susceptibles in a population (=1/ R₀).
The critical vaccination threshold, which is equal to 1-(1/ R₀), and determines the number of people that must be vaccinated to prevent the spread of a pathogen.

Disability-adjusted life years (DALY)

The number of years of life lost due to premature mortality plus the years lost due to disability while infected. Used to quantify the burden of disease.

Epidemic

An outbreak of an infectious disease, such that a greater number of individuals than normal has the disease. Epidemics have very high R₀ (Recall R₀>1 for a disease to spread) and are often associated with acute, highly transmissible pathogens that can be directly transmitted. Further, pathogens with lower infectious periods create more explosive epidemics.

To control epidemics, it is necessary to reduce R₀. This can be done by:
- Reducing transmissibility.
- Decreasing the number of susceptibles (by vaccination, for example).
- Decreasing the mean number of contacts or the transmissibility, such as by improving sanitation, or limiting the number of interactions sick people have with healthy people.
- Reducing length of infectious period.

Force of infection (FoI)

A measure of the degree to which an infected individual can spread infection; the per-capita rate at which susceptibles contract infection. Typically increases with transmissibility and prevalence of infection.

Herd immunity

Vaccines protect individuals, but also provide indirect protection to anyone those vaccinated people may have infected. The portion of the population that needs to be immunized in order to achieve herd immunity is P > 1 – (1/ R₀), where P = proportion vaccinated * vaccine efficacy.

Incidence (of a pathogen)

The number of new cases or infections in a given time period.

Immune

Unable to become infected/infectious

Prevalence (of a pathogen)

The proportion of a population that is infectious at any given time.

Transmissibility ($\beta$)

Also known as the effective contact rate, is the product of the contact rate and the probability of transmission per contact.

Virulence

The capacity of a pathogen to produce disease. It is proportional to parasitemia, or the number of circulating copies of the pathogen in the host. The higher the virulence (given contact between S and I individuals), the more likely transmission is to occur. However, higher virulence means contact may be less likely as infected hosts show more symptoms of the disease. There is a trade-off that occurs between high transmissibility and disease-induced mortality.

How to use the software¶

This section contains detailed procedural information about how to use Epidemiological MODeling software (EMOD). For example, how to download input data files, how to set up groups in a population, how to create a configuration file and campaign file, how to run simulations both locally and on a remote HPC cluster, and more.

The architecture diagram below shows, at a high level, how the system functions.

High-level EMOD system architecture¶

For more information about how each component of the system fits together, see Introduction to the software. That section provides a conceptual overview of EMOD and describes each required or optional component for running a simulation.

Use input data files¶

The input data files contain the relatively fixed information about the population within each geographic node. For example, the number of individuals, geography, climate, demographics, and migration data. This topic describes how to download and use the input data files. These are in contrast to the demographic, geography, and migration parameters in the configuration file that control simulation-wide qualities, such as enabling air migration across all nodes in the simulation.

Except for the demographics file, you will generally use input data files without modifying them in any way. Only the demographics file is required, though migration files may be required for multi-node simulations. See Input data files for an overview of each of the different input files, including which are required for different simulations. See Input data file structure for reference information about the structure of each of these files.

Download input files¶

The EMOD-InputData repository uses large file storage (LFS) to manage the binaries and large JavaScript Object Notation (JSON) files. A standard Git clone of the repository will only retrieve the metadata for these files managed with LFS. To retrieve the actual data, follow the steps below.

Install the Git LFS plugin, if it is not already installed.
- For Windows users, download the plugin from https://git-lfs.github.com.
- For CentOS on Azure users, the plugin is included with the PrepareLinuxEnvironment.sh script.
Using a Git client or Command Prompt window, clone the input data repository to retrieve the metadata:
```
git clone https://github.com/InstituteforDiseaseModeling/EMOD-InputData.git
```
Navigate to the directory where you downloaded the metadata for the input data files.
Cache the actual data on your local machine:
```
git lfs fetch
```
Replace the metadata in the files with the actual contents:
```
git lfs checkout
```

Specify input files in the configuration file¶

Follow the steps below to specify which input files to use in a simulation. Only the demographics file is required, though additional files are generally needed for a realistic simulation.

Place all input files for a simulation in the same directory. You will specify this directory when you run a simulation. See Run simulations for more information.
In your configuration file, specify the path to each of these files, relative to the directory above, in the appropriate parameter. Generally, these parameters are appended with “_Filename” or “_Filenames”.

For example, the example below shows the relevant portion of a configuration file. See :doc :parameter-configuration for a complete list of the parameters.

{
    "parameters": {
        "Air_Temperature_Filename": "Namawala_single_node_air_temperature_daily.bin",
        "Air_Temperature_Offset": 0,
        "Air_Temperature_Variance": 2,
        "Base_Rainfall": 100,
        "Campaign_Filename": "campaign.json",
        "Climate_Model": "CLIMATE_BY_DATA",
        "Climate_Update_Resolution": "CLIMATE_UPDATE_DAY",
        "Config_Name": "VectorAndMalaria_5_Namawala_Vector_ITNs",
        "Demographics_Filenames": [
            "Namawala_single_node_demographics.json"
        ],
        "Geography": "Namawala",
        "Land_Temperature_Filename": "Namawala_single_node_land_temperature_daily.bin",
        "Land_Temperature_Offset": 0,
        "Land_Temperature_Variance": 2,
        "Load_Balance_Filename": "",
        "Rainfall_Filename": "Namawala_single_node_rainfall_daily.bin",
        "Rainfall_In_mm_To_Fill_Swamp": 1000.0,
        "Rainfall_Scale_Factor": 1,
        "Relative_Humidity_Filename": "Namawala_single_node_relative_humidity_daily.bin",
        "Relative_Humidity_Scale_Factor": 1,
        "Relative_Humidity_Variance": 0.05
    }
}

Modify demographics files¶

The demographics files provided by IDM generally contain information about prevalence, immunity, risk, population size, and more for a geographic region. However, you will almost certainly want to modify the file to provide more detail or to set up groups within a population to more accurately model heterogeneous populations in terms of transmission, group transitions, or targeted interventions.

The demographics file is the only required input data file, with one exception. You have the option to run a simulation without a demographics file if you set Enable_Demographics_Builtin to 1 in the configuration file. However, this option is primarily used for software testing. It will not provide meaningful simulation data as it does not represent the population of a real geographic location.

Use demographics overlay files¶

You can use multiple demographic files when setting up a simulation. The “base layer” file contains default parameter settings and one or more overlay files contains additional parameters or different parameter values that override the values in the default file. This topic describes how to set up the base layer and overlay files.

Demographic overlay files allow you to do the following:

Separate different sets of parameters and values into individual overlays (for example, to separate those that are useful for specific diseases into different overlay files)
Add new parameters for a simulation into an overlay for easier prototyping
Override certain parameters of interest in an overlay
Override certain parameters for a particular sub-region
Simulating subsets of a larger region for which input data files have been constructed

Select the demographics file to use as the base layer file. All nodes to be included in the simulation must be listed in this file.
In the metadata, make note of the IdReference value.

You may change this value if you desire, but all input files for a simulation must have the same IdReference value. For more information about this parameter and the structure of demographics files in general, see Demographics parameters.
Create one or more overlay files. Keep the following things in mind:
- In the metadata, the value for IdReference must match the value in the base layer file and all other input data files.
- Any nodes listed in an overlay but not in the base layer will not be simulated.
- If the demographics files include any JSON array elements, the entire array is overridden. You cannot add or remove individual elements in an array using an overlay file.
- Overriding a parameter value on a node will not affect the other parameter values applied to that node.
- Values set in the Defaults section of an overlay will be applied to all nodes listed in that file, not all nodes in the entire simulation. Therefore, an overlay file that includes a Defaults section but no Nodes section will not have any effect.
Place all demographics files in the directory where the other input data files are stored.
In the configuration file, set Demographics_Filenames to an array that contains a comma-delimited list of demographics files, listing the base layer file first.

An example base layer demographics file and an overlay file is below. You can see that the overlay adds the TransmissionMatrix for Heterogeneous Intra-Node Transmission (HINT) to only three of the five nodes (which correspond to Washington state counties).

{
    "Metadata": {
        "Author": "ewenger",
        "NodeCount": 5,
        "Tool": "table_to_demographics.py",
        "IdReference": "SampleContent",
        "DateCreated": "2013-08-01 15:37:16.853000"
    },
    "Defaults": {
        "NodeAttributes": {
            "BirthRate_DESCRIPTION": "Replacement of stable age distribution: Birth_Rate_Dependence=DEMOGRAPHIC_DEP_RATE (i.e. 14-45 year-old PossibleMothers)",
            "BirthRate": 0.00017675,

            "Airport": 0,
            "Region": 1,
            "Altitude": 0,
            "Seaport": 0
        },
        "IndividualAttributes": {

            "AgeDistribution_DESCRIPTION": "Box between age 0 and 60 years: Age_Initialization_Distribution_Type=DISTRIBUTION_SIMPLE",
            "AgeDistributionFlag": 1,
            "AgeDistribution1": 0,
            "AgeDistribution2": 21900,

            "PrevalenceDistribution_DESCRIPTION": "No initial infections",
            "PrevalenceDistributionFlag": 0,
            "PrevalenceDistribution1": 0,
            "PrevalenceDistribution2": 0,

            "RiskDistributionFlag": 0,
            "RiskDistribution1": 1,
            "RiskDistribution2": 0,

            "ImmunityDistributionFlag": 0,
            "ImmunityDistribution1": 1,
            "ImmunityDistribution2": 0,

            "MigrationHeterogeneityDistributionFlag": 0,
            "MigrationHeterogeneityDistribution1": 1,
            "MigrationHeterogeneityDistribution2": 0,

            "MortalityDistribution_DESCRIPTION": "WA state (1999-2010).  Source: wonder.cdc.gov",
            "MortalityDistribution": {
                "NumDistributionAxes": 2,
                "AxisNames": [ "gender", "age" ],
                "AxisUnits": [ "male=0,female=1", "years" ],
                "AxisScaleFactors": [ 1, 365 ],
                "NumPopulationGroups": [ 2, 15 ],
                "PopulationGroups": [
                    [ 0, 1 ],
                    [ 0, 1, 2.5, 7.5, 12.5, 17.5, 22.5, 30, 40, 50, 60, 70, 80, 90, 120 ]
                ],
                "ResultUnits": "annual deaths per 1000 individuals",
                "ResultScaleFactor": 0.00000273972602739726027397260273973,
                "ResultValues": [
                    [ 4.8, 0.2, 0.1, 0.2, 0.5, 1.1, 1.1, 1.7, 4.1, 9.2, 19.8, 53.7, 154.2, 1000, 1000 ],
                    [ 4.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.5, 1.1, 2.6, 5.5, 14.3, 40.1, 129.3, 1000, 1000 ]
                ]
            }
        }
    },
    "Nodes": [
        {
            "County": "Adams",
            "NodeAttributes": {
                "Latitude": 47.1274,
                "InitialPopulation": 19200,
                "Longitude": -118.38
            },
            "NodeID": 1
        },
        {
            "County": "Asotin",
            "NodeAttributes": {
                "Latitude": 46.3393,
                "InitialPopulation": 21800,
                "Longitude": -117.0482
            },
            "NodeID": 2
        },
        {
            "County": "Benton",
            "NodeAttributes": {
                "Latitude": 46.2068,
                "InitialPopulation": 183400,
                "Longitude": -119.7689
            },
            "NodeID": 3
        },
        {
            "County": "Chelan",
            "NodeAttributes": {
                "Latitude": 47.4235,
                "InitialPopulation": 73600,
                "Longitude": -120.3103
            },
            "NodeID": 4
        },
        {
            "County": "Clallam",
            "NodeAttributes": {
                "Latitude": 48.1181,
                "InitialPopulation": 72350,
                "Longitude": -123.4307
            },
            "NodeID": 5
        }
    ]
}

{
    "Metadata": {
        "Author": "cwiswell",
        "NodeCount": 10,
        "Tool": "table_to_demographics.py",
        "IdReference": "SampleContent",
        "DateCreated": "2013-08-01 15:37:16.853000"
    },
    "Defaults": {
        "IndividualProperties": [
            {
                "Property": "Accessibility",
                "Values": [ "VaccineTake", "VaccineRefuse"],
                "Initial_Distribution": [ 0.85, 0.15],
                "Transitions": [],
                "TransmissionMatrix": {
                    "Route": "Contact",
                    "Matrix": [
                        [1.1, 0.3],
                        [0.3, 5.0]
                    ]
                }
            },
            {
                "Property": "Age_Bin",
                "Age_Bin_Edges_In_Years": [ 0, 5, 13, -1 ],
                "Transitions": [],
                "TransmissionMatrix": {
                    "Route": "Contact",
                    "Matrix": [
                        [1.4, 1.0, 1.0],
                        [1.0, 2.5, 0.7],
                        [1.0, 0.7, 1.0]
                    ]
                }
            }
        ]
    },
    "Nodes": [
        {
            "NodeID": 1
        },
        {
            "NodeID": 3
        },
        {
            "NodeID": 5
        }
    ]
}

Configure heterogeneity using individual and node properties¶

Demographics files can be used to add heterogeneity to a population. You can define property values for accessibility, age, geography, risk, and other properties and assign these values to individuals or nodes in the simulation. For more information about the parameters and structure of demographics files, see Demographics parameters.

These properties are most commonly used to target (or avoid targeting) particular nodes or individuals with interventions. For example, you might want to put individuals into different age bins and then target interventions to individuals in a particular age bin. Another common use is to configure treatment coverage to be higher for nodes that are easy to access and lower for nodes that are difficult to access. For more information on targeting interventions to particular nodes or individuals, see Target interventions to nodes or individuals.

For the generic simulation type, you can also configure heterogeneous transmission between individuals with different property values. For more information, see Configure heterogeneous disease transmission. For other simulation types, transmission is configured using mechanistic parameter settings.

This topic describes how to define individual properties and assign different values to individuals in a simulation. However, with the exception of setting up age bins, you can use the same process to configure heterogeneity using NodeProperties. To see all individual and node property parameters, see NodeProperties and IndividualProperties.

Create individual properties other than age¶

Assigning property values, such as accessibility or risk, to individuals uses the IndividualProperties parameter in the demographics file. See Demographics parameters for a list of supported properties. The values you assign to properties are user-defined and can be applied to individuals in all nodes or only in particular nodes in a simulation.

In the demographics file, add the IndividualProperties parameter and set it to an empty array. If you want the values to apply to all nodes, add it in the Defaults section; if you want the values to be applied to specific nodes, add it to the Nodes section.
In the array, add an empty JSON object. Within it, do the following:
1. Add the Property parameter and set it to one of the supported values.
2. Add the Values parameter and set it to an array of possible values that can be assigned to individuals. You can define any value here.
3. Add the Initial_Distribution parameter and set it to an array of numbers that add up to 1. This configures the initial distribution of the values assigned to individuals in one or all nodes.
4. To define how individuals transition from one property value to another, add the Transitions parameter and set it to it an empty array. Within it, do the following:
  1. Add an empty JSON object and set parameters that define the value that individuals transition from, the value they transition to, the event that triggers the transition, the probability of transition, and more. See Demographics parameters for a list of supported Transitions parameters and values.
If you want to add another property and associated values, add a new JSON object in the IndividualProperties array as above.

Note

Multiple properties must be defined in one file. They can be defined in either the base layer demographics file or an overlay file, but they cannot be split between the files. The maximum number of property types that can be added is two.

Create properties for age ranges¶

Creating properties based on age ranges works a little differently than other properties. Age_Bin is tied to the simulated age of an individual rather than being an independent property. Some of its characteristics, such as initial distribution and transitions, are dependent on information from the demographics file and EMOD that manages individual aging during the simulation. The Transitions array is not required and other parameters and structures in IndividualProperties are slightly different.

In the demographics file, add the IndividualProperties parameter and set it to an empty array. If you want the values to apply to all nodes, add it in the Defaults section; if you want the values to be applied to specific nodes, add it to the Nodes section.
In the array, add an empty JSON object. Within it, do the following:
1. Add the Property parameter and set it to “Age_Bin”.
2. Add the Age_Bin_Edges_In_Years parameter and set it to an array that contains a comma- delimited list of integers in ascending order that define the boundaries used for each of the age bins, in years. The first number must always be 0 (zero) to indicate the age at birth and the last number must be -1 to indicate the maximum age in the simulation.

The example below shows how to set up several property values based on disease risk and physical place, and how to move individuals among these values. It also defines three age bins: 0 to 5 years, older than 5 to 13, and older than 13 to the maximum age.

{
    "Defaults": {
        "IndividualProperties": [{
            "Property": "Risk",
            "Values": ["Low", "Medium", "High"],
            "Initial_Distribution": [0.7, 0.2, 0.1],
            "Transitions": [{
                "From": "High",
                "To": "Medium",
                "Type": "At_Age",
                "Coverage": 1,
                "Probability_Per_Timestep": 0.3,
                "Timestep_Restriction": 20,
                "Age_In_Years": 5,
                "Timesteps_Until_Reversion": 0
            }, {
                "From": "Medium",
                "To": "Low",
                "Type": "At_Age",
                "Coverage": 1,
                "Probability_Per_Timestep": 0.3,
                "Timestep_Restriction": 20,
                "Age_In_Years": 12,
                "Timesteps_Until_Reversion": 0
            }]
        }, {
            "Property": "Place",
            "Values": ["Community", "School", "Work", "Vacation"],
            "Initial_Distribution": [0.3, 0.2, 0.4, 0.1],
            "Transitions": [{
                "From": "School",
                "To": "Vacation",
                "Type": "At_Timestep",
                "Coverage": 1,
                "Timestep_Restriction": {
                    "Start": 20
                },
                "Age_In_Years_Restriction": {},
                "Probability_Per_Timestep": 1,
                "Timesteps_Until_Reversion": 20
            }]
        }, {
            "Property": "Age_Bin",
            "Age_Bin_Edges_In_Years": [0, 5, 13, -1],
            "Transitions": []
        }]
    }
}

Configure heterogeneous disease transmission¶

Only generic simulations can use Heterogeneous Intra-Node Transmission (HINT) to manually configure heterogeneous disease transmission within each node. All other simulation types have preconfigured solutions for heterogeneous disease transmission based on the biological processes of the disease being modeled. Because HINT cannot be used with this simulation type, the parameter Enable_Heterogeneous_Intranode_Transmission in the configuration file must be set to 0 (zero).

Heterogeneity and transmission scaling¶

This topic describes the mathematics governing the Heterogeneous Intra-Node Transmission (HINT) feature. However, only generic simulations can use HINT to manually configure heterogeneous disease transmission within each node. All other simulation types have preconfigured solutions for heterogeneous disease transmission based on the biological processes of the disease being modeled. See the generic disease documentation for more information about HINT.

Create a configuration file¶

You define the general configuration and processing of a simulation through a JSON-formatted configuration file, typically called config.json. Two forms of the simulation configuration file are used: a flattened version and a hierarchical version. This topic describes how to create a configuration file of other form. The EMOD executable (Eradication.exe) and Eradication binary requires a flattened version of the simulation configuration file. The hierarchical version allows you to organize parameters into logical groups, making them easier to manage. If you use hierarchical configuration files, you must flatten them prior to running a simulation.

The EMOD Regression directory contains many different subdirectories that contain configuration, campaign, and other associated files to run simulations that represent real-world scenarios. Within the each subdirectory, there is usually a hierarchical overlay file (param_overrides.json) and a flattened file (config.json), which has been created by combining param_overrides.json with one of the default files in Regression/defaults. The default files are also organized hierarchically. The naming of these files is an arbitrary convention used at IDM; you may name this files anything you choose. See Use configuration overlay files for more information about flattening hierarchical files.

For a complete list of configuration parameters that are available to use with this simulation type, see Configuration parameters. For more information about JSON, see EMOD parameter reference.

Flattened configuration file¶

A flattened configuration file is generally a single-depth JSON file with configuration parameters listed alphabetically. This is the configuration file format that Eradication.exe requires for running simulations.

Below is an example of a flattened configuration file:

{
    "parameters": {
        "Acquisition_Blocking_Immunity_Decay_Rate": 0.1,
        "Acquisition_Blocking_Immunity_Duration_Before_Decay": 60,
        "Age_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE",
        "Animal_Reservoir_Type": "NO_ZOONOSIS",
        "Base_Incubation_Period": 0,
        "Base_Individual_Sample_Rate": 1,
        "Base_Infectious_Period": 4,
        "Base_Infectivity": 3.5,
        "Base_Mortality": 0,
        "Base_Population_Scale_Factor": 1,
        "Birth_Rate_Dependence": "POPULATION_DEP_RATE",
        "Birth_Rate_Time_Dependence": "NONE",
        "Burnin_Cache_Mode": "none",
        "Burnin_Cache_Period": 0,
        "Burnin_Name": "",
        "Campaign_Filename": "campaign.json",
        "Climate_Model": "CLIMATE_OFF",
        "Config_Name": "00_DEFAULT",
        "Custom_Reports_Filename": "NoCustomReports",
        "Death_Rate_Dependence": "NOT_INITIALIZED",
        "Default_Geography_Initial_Node_Population": 1000,
        "Default_Geography_Torus_Size": 10,
        "Demographics_Filenames": [
            "../00_Default/demographics.json"
        ],
        "Enable_Absolute_Time": "NO",
        "Enable_Aging": 1,
        "Enable_Birth": 1,
        "Enable_Default_Reporting": 1,
        "Enable_Default_Shedding_Function": 1,
        "Enable_Demographics_Birth": 0,
        "Enable_Demographics_Builtin": 0,
        "Enable_Demographics_Gender": 1,
        "Enable_Demographics_Risk": 0,
        "Enable_Demographics_Reporting": 1,
        "Enable_Disease_Mortality": 0,
        "Enable_Heterogeneous_Intranode_Transmission": 0,
        "Enable_Immune_Decay": 0,
        "Enable_Immunity": 1,
        "Enable_Interventions": 1,
        "Enable_Maternal_Infection_Transmission": 0,
        "Enable_Natural_Mortality": 0,
        "Enable_Property_Output": 0,
        "Enable_Spatial_Output": 0,
        "Enable_Superinfection": 0,
        "Enable_Vital_Dynamics": 0,
        "Geography": "",
        "Immunity_Acquisition_Factor": 0,
        "Immunity_Initialization_Distribution_Type": "DISTRIBUTION_OFF",
        "Immunity_Mortality_Factor": 0,
        "Immunity_Transmission_Factor": 0,
        "Incubation_Period_Distribution": "FIXED_DURATION",
        "Individual_Sampling_Type": "TRACK_ALL",
        "Infection_Updates_Per_Timestep": 1,
        "Infectious_Period_Distribution": "EXPONENTIAL_DURATION",
        "Infectivity_Scale_Type": "CONSTANT_INFECTIVITY",
        "Job_Node_Groups": "Chassis08",
        "Job_Priority": "BELOWNORMAL",
        "Listed_Events": [],
        "Load_Balance_Filename": "",
        "Local_Simulation": 0,
        "Maternal_Transmission_Probability": 0,
        "Max_Individual_Infections": 1,
        "Max_Node_Population_Samples": 40,
        "Migration_Model": "NO_MIGRATION",
        "Minimum_Adult_Age_Years": 15,
        "Mortality_Blocking_Immunity_Decay_Rate": 0.001,
        "Mortality_Blocking_Immunity_Duration_Before_Decay": 60,
        "Mortality_Time_Course": "DAILY_MORTALITY",
        "Node_Grid_Size": 0.042,
        "Num_Cores": 1,
        "Number_Basestrains": 1,
        "Number_Substrains": 1,
        "PKPD_Model": "FIXED_DURATION_CONSTANT_EFFECT",
        "Population_Density_C50": 30,
        "Population_Density_Infectivity_Correction": "CONSTANT_INFECTIVITY",
        "Population_Scale_Type": "USE_INPUT_FILE",
        "Report_Event_Recorder": 0,
        "Run_Number": 1,
        "Sample_Rate_0_18mo": 1,
        "Sample_Rate_10_14": 1,
        "Sample_Rate_15_19": 1,
        "Sample_Rate_18mo_4yr": 1,
        "Sample_Rate_20_Plus": 1,
        "Sample_Rate_5_9": 1,
        "Sample_Rate_Birth": 1,
        "Serialization_Test_Cycles": 0,
        "Simulation_Duration": 90,
        "Simulation_Timestep": 1,
        "Simulation_Type": "GENERIC_SIM",
        "Start_Time": 0,
        "Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY",
        "Transmission_Blocking_Immunity_Decay_Rate": 0.1,
        "Transmission_Blocking_Immunity_Duration_Before_Decay": 60,
        "x_Air_Migration": 1,
        "x_Birth": 1,
        "x_Local_Migration": 1,
        "x_Other_Mortality": 1,
        "x_Population_Immunity": 1,
        "x_Regional_Migration": 1,
        "x_Sea_Migration": 1,
        "x_Temporary_Larval_Habitat": 1
    }
}

Hierarchical configuration file¶

The hierarchical version of a configuration file has a more complex structure. As a way of sorting parameters into logical groups, the parameters can be contained inside nested JSON objects. The names you use to create these logical categories are unimportant; the scripts used to flatten the files will search through the hierarchies and retain only the “leaf” values in the resulting flattened file. See Use configuration overlay files for more information on flattening files.

Below is an example of a hierarchical configuration file:

{
    "parameters": {
        "CAMPAIGNS": {
            "Campaign_Filename": "campaign.json",
            "Enable_Interventions": 1,
            "Listed_Events": [],
            "PKPD_Model": "FIXED_DURATION_CONSTANT_EFFECT"
        },
        "CLIMATE": {
            "Climate_Model": "CLIMATE_OFF"
        },
        "DEMOGRAPHICS": {
            "Age_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE",
            "Base_Population_Scale_Factor": 1,
            "Birth_Rate_Dependence": "DEMOGRAPHIC_DEP_RATE",
            "Birth_Rate_Time_Dependence": "NONE",
            "Demographics_Filenames": ["NO_DEFAULT_DEMOGRAPHICS"],
            "Default_Geography_Initial_Node_Population": 1000,
            "Default_Geography_Torus_Size": 10,
            "Enable_Aging": 1,
            "Enable_Birth": 1,
            "Enable_Demographics_Birth": 0,
            "Enable_Demographics_Gender": 1,
            "Enable_Demographics_Builtin": 0,
            "Enable_Demographics_Risk": 0,
            "Enable_Demographics_Reporting": 0,
            "Enable_Natural_Mortality": 1,
            "Enable_Vital_Dynamics": 1,
            "Minimum_Adult_Age_Years": 15,
            "IMMUNITY": {
                "Acquisition_Blocking_Immunity_Decay_Rate": 0.1,
                "Acquisition_Blocking_Immunity_Duration_Before_Decay": 60,
                "Enable_Immune_Decay": 1,
                "Enable_Immunity": 1,
                "Immunity_Acquisition_Factor": 0,
                "Immunity_Transmission_Factor": 0,
                "Immunity_Initialization_Distribution_Type": "DISTRIBUTION_OFF",
                "Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY",
                "Transmission_Blocking_Immunity_Decay_Rate": 0.1,
                "Transmission_Blocking_Immunity_Duration_Before_Decay": 60
            },
            "MORTALITY": {
                "Base_Mortality": 0,
                "Enable_Disease_Mortality": 0,
                "Death_Rate_Dependence": "NONDISEASE_MORTALITY_BY_AGE_AND_GENDER",
                "Immunity_Mortality_Factor": 0,
                "Mortality_Blocking_Immunity_Decay_Rate": 0.001,
                "Mortality_Blocking_Immunity_Duration_Before_Decay": 60,
                "Mortality_Time_Course": "DAILY_MORTALITY"
            },
            "Population_Density_C50": 30,
            "Population_Scale_Type": "USE_INPUT_FILE",
            "SAMPLING": {
                "Base_Individual_Sample_Rate": 1,
                "Individual_Sampling_Type": "TRACK_ALL",
                "Max_Node_Population_Samples": 40,
                "Sample_Rate_0_18mo": 1,
                "Sample_Rate_10_14": 1,
                "Sample_Rate_15_19": 1,
                "Sample_Rate_18mo_4yr": 1,
                "Sample_Rate_20_Plus": 1,
                "Sample_Rate_5_9": 1,
                "Sample_Rate_Birth": 2
            }
        },
        "DISEASE": {
            "Animal_Reservoir_Type": "NO_ZOONOSIS",
            "Enable_Superinfection": 0,
            "INCUBATION": {
                "Base_Incubation_Period": 3,
                "Incubation_Period_Distribution": "FIXED_DURATION"
            },
            "INFECTIOUSNESS": {
                "Base_Infectious_Period": 7,
                "Base_Infectivity": 0.3,
                "Infectious_Period_Distribution": "EXPONENTIAL_DURATION",
                "Infectivity_Scale_Type": "CONSTANT_INFECTIVITY",
                "Population_Density_Infectivity_Correction": "CONSTANT_INFECTIVITY"
            },
            "Infection_Updates_Per_Timestep": 1,
            "Max_Individual_Infections": 1,
            "TRANSMISSION": {
                "Enable_Maternal_Infection_Transmission": 0,
                "Maternal_Transmission_Probability": 0
            }
        },
        "FUDGE_FACTORS": {
            "x_Air_Migration": 1,
            "x_Birth": 1,
            "x_Local_Migration": 1,
            "x_Other_Mortality": 1,
            "x_Population_Immunity": 1,
            "x_Regional_Migration": 1,
            "x_Sea_Migration": 1,
            "x_Temporary_Larval_Habitat": 1
        },
        "HPC": {
            "Job_Node_Groups": "Chassis08",
            "Job_Priority": "BELOWNORMAL",
            "Load_Balance_Filename": "",
            "Local_Simulation": 0,
            "Num_Cores": 1
        },
        "INTRANODE_TRANSMISSION": {
            "Enable_Default_Shedding_Function": 1,
            "Enable_Heterogeneous_Intranode_Transmission": 0
        },
        "MIGRATION": {
            "Migration_Model": "NO_MIGRATION"
        },
        "OUTPUT": {
            "Custom_Reports_Filename": "NoCustomReports",
            "Report_Event_Recorder": 0,
            "Enable_Default_Reporting": 1,
            "Enable_Property_Output": 0,
            "Enable_Spatial_Output": 0
        },
        "POLIO": {},
        "PRIMARY": {
            "Config_Name": "DEFAULT_CONFIG_NAME_SHOULD_BE_SET",
            "ENUMS": {
                "Simulation_Type": "GENERIC_SIM"
            },
            "Geography": "DEFAULT_GEOGRAPHY_SHOULD_BE_SET",
            "Node_Grid_Size": 0.042,
            "Run_Number": 0,
            "Simulation_Duration": 365,
            "Simulation_Timestep": 1,
            "Start_Time": 0,
            "Enable_Absolute_Time": "NO"

        },
        "SERIALIZATION": {
            "Burnin_Cache_Mode": "none",
            "Burnin_Cache_Period": 0,
            "Burnin_Name": "",
            "Serialization_Test_Cycles": 0
        },
        "STRAIN_TRACKING": {
            "Number_Basestrains": 1,
            "Number_Substrains": 1
        }
    }
}

Create or modify configuration files¶

Although you can create configuration files entirely from scratch, it is often easier to start from an existing configuration file and modify it to meet your needs. Any of the configuration files in the Regression directory may be used; in particular the Regression/defaults directory contains hierarchical configuration files with the common parameter settings used with different simulation types.

The simplest method is to use a text editor to directly edit the parameters or parameter values in the JSON file. However, while direct editing of files may be sufficient for small and infrequent changes, you will likely find that scripting tools are more powerful and reliable for both creating and modifying files.

The following example shows how to read a configuration file to a Python dictionary, modify a parameter, and write it back out to the file:

import json

# load the current config.json
config_file = open( "config.json" )
config_json = json.load( config_file )
config_file.close()

# modify one of the parameter values, e.g. "base_infectivity"
config_json["parameters"]["base_infectivity"] = 0.5

# write the modified config file
modified_file = open( "modified_config.json", "w" )
json.dump( config_json, modified_file, sort_keys=True, indent=4 )
modified_file.close()

The following example shows how to modify a configuration file in MATLAB:

addpath Matlab
addpath Matlab\test

% load the simulation configuration file into MATLAB structure
configjson = loadJson( "config.json" );

% modify one of the values
configjson.parameters.Base_Infectivity = 08.5;

% save the new configuration to file
saveJson( "modified_config.json", configjson );

Generate a list of all available parameters (a schema)¶

You can generate a schema from the EMOD executable (Eradication.exe) or Eradication binary that defines all configuration parameters and campaign parameters available in the version of EMOD that is installed, for all available simulation types. This includes parameter names, data types, defaults, ranges, and short descriptions.

Logging information is also produced as part of the schema. If you are using EMOD source and have added or modified configuration parameters or intervention code, this logging information can help you troubleshoot any errors that may occur.

Warning

If you modify the source code to add or remove configuration or campaign parameters, you may need to update the code used to produce the schema. You must also verify that your simulations are still scientifically valid.

Command options¶

The following command-line options are available for providing information about EMOD.

EMOD information commands¶
Long form	Short form	Description
`--help`	`-help`	Shows help options for Eradication.exe.
`--version`	`-v`	Shows the version information and supported simulation types. Note capitalization of short alternative.
`--get-schema`	`--get-schema`	Outputs the configuration and campaign parameters. Unless `--schema-path` or a redirect operator is used, the schema will print to the Command Prompt window.
`--schema-path`	`>`	When used with `--get-schema`, if a TXT or JSON file is specified, the schema information will be written to the file instead of the Command Prompt window.

Usage¶

Open a Command Prompt window and navigate to the directory where Eradication.exe is installed.
To output the schema to the Command Prompt window, enter:
```
Eradication.exe --get-schema
```
To output the schema to a file, do one of the following (replacing <filename> with the desired filename):
- To output a text file that includes logging information, enter:
```
Eradication.exe --get-schema > <filename>.txt
```
- To display logging in the Command Prompt window and output a text file that does not include logging information, enter:
```
Eradication.exe --get-schema --schema-path <filename>.txt
```
- To output the schema to a JSON file that includes logging information, enter:
```
Eradication.exe --get-schema > <filename>.json
```
- To display logging in the Command Prompt window and output a JSON file that does not include logging information, enter:
```
Eradication.exe --get-schema --schema-path <filename>.json
```

Perform a parameter sweep¶

Parameter sweeps iteratively update the values of parameters to exhaustively search through the parameter space for a simulation. EMOD does not currently support automated parameter sweeps. However, you can write your own code, such as a Python or MATLAB script, that iterates through the values you want for a particular parameter. This topic describes how to perform a parameter sweep.

For example, you can run simulations using a Python script that uses the parameter values specified in a JSON parameter sweep file to iteratively update the configuration or campaign parameter values. The IDM test team performs parameter sweeps as part of regression testing. See the following examples to see how this is implemented.

The following JSON example illustrates sweeping through configuration parameter values.

{
    "sweep" :
    {
        "path": "43_Vector_Garki_MultiCore_VectorMigration",
        "param_name" : "Run_Number",
        "param_values" : [ 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144 ]
    }
}

The following Python example illustrates how to update the configuration file with the above values. This is excerpted from the regression_test.py script in the Regression directory.

elif "sweep" in reglistjson:
    print( "Running sweep...\n" )
    param_name = reglistjson["sweep"]["param_name"]

    for param_value in reglistjson["sweep"]["param_values"]:
        os.chdir(ru.cache_cwd)
        sim_timestamp = str(datetime.datetime.now()).replace('-', '_' ).replace( ' ', '_' ).replace( ':', '_' ).replace( '.', '_' )
        if regression_id == None:
            regression_id = sim_timestamp

        configjson = ru.flattenConfig( os.path.join( reglistjson["sweep"]["path"], "param_overrides.json" ) )
        if configjson is None:
            print("Error flattening config.  Skipping " + simcfg["path"])
            ru.final_warnings += "Error flattening config.  Skipped " + simcfg["path"] + "\n"
            continue

        # override sweep parameter
        configjson["parameters"][param_name] = param_value

        campjson_file = open( os.path.join( reglistjson["sweep"]["path"],"campaign.json" ) )
        campjson = json.loads( campjson_file.read().replace( "u'", "'" ).replace( "'", '"' ).strip( '"' ) )
        campjson_file.close()
        configjson["campaign_json"] = str(campjson)

        report_fn = os.path.join( reglistjson["sweep"]["path"],"custom_reports.json" )
        if os.path.exists( report_fn ) == True:
            reportjson_file = open( report_fn )
            reportjson = json.loads( reportjson_file.read().replace( "u'", "'" ).replace( "'", '"' ).strip( '"' ) )
            reportjson_file.close()
            configjson["custom_reports_json"] = str(reportjson)
        else:
            configjson["custom_reports_json"] = None

        thread = runner.commissionFromConfigJson( sim_timestamp, configjson, reglistjson["sweep"]["path"], None, 'sweep' )
        ru.reg_threads.append( thread )
else:
    print "Unknown state"
    sys.exit(0)

Use configuration overlay files¶

You can use two configuration files when setting up a simulation. One file contains default parameter settings and an overlay file contains additional parameters or different parameter values that override the values in the default file. This topic describes how to set up the default and overlay file and flatten them into a single file before running a simulation.

Overlay files allow you to easily separate a subset of parameters that are of particular interest from the rest of the parameters needed to run a simulation. You can easily modify the parameters in the overlay file without needing to maintain a complete configuration or campaign file. This can be especially helpful when you want to experiment with the values set in certain parameters of interest without modifying the rest of the settings. You can have one default file and many different overlay files for different configurations. It also allows you to easily update the default values across multiple simulations.

In addition to being used for model experimentation, overlay files are used when testing the software functionality after making source code changes. If you run the EMOD regression tests using regression_test.py, configuration files will be flattened as part of those tests. However, this may take several hours if run locally. More guidance on modifying the EMOD source code is in the “Advance the model” section.

Follow the steps below to quickly flatten configuration files.

Create the default configuration file in JSON. You may, though it is not required, organize the parameters into logical categories of nested JSON objects to make managing the parameters easier. See Configuration parameters for a complete list of all parameters that are available. See the example default configuration file below.

{
    "parameters": {
        "CAMPAIGNS": {
            "Campaign_Filename": "campaign.json",
            "Enable_Interventions": 1,
            "Listed_Events": [],
            "PKPD_Model": "FIXED_DURATION_CONSTANT_EFFECT"
        },
        "CLIMATE": {
            "Climate_Model": "CLIMATE_OFF"
        },
        "DEMOGRAPHICS": {
            "Age_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE",
            "Base_Population_Scale_Factor": 1,
            "Birth_Rate_Dependence": "DEMOGRAPHIC_DEP_RATE",
            "Birth_Rate_Time_Dependence": "NONE",
            "Demographics_Filenames": ["NO_DEFAULT_DEMOGRAPHICS"],
            "Default_Geography_Initial_Node_Population": 1000,
            "Default_Geography_Torus_Size": 10,
            "Enable_Aging": 1,
            "Enable_Birth": 1,
            "Enable_Demographics_Birth": 0,
            "Enable_Demographics_Gender": 1,
            "Enable_Demographics_Builtin": 0,
            "Enable_Demographics_Risk": 0,
            "Enable_Demographics_Reporting": 0,
            "Enable_Natural_Mortality": 1,
            "Enable_Vital_Dynamics": 1,
            "Minimum_Adult_Age_Years": 15,
            "IMMUNITY": {
                "Acquisition_Blocking_Immunity_Decay_Rate": 0.1,
                "Acquisition_Blocking_Immunity_Duration_Before_Decay": 60,
                "Enable_Immune_Decay": 1,
                "Enable_Immunity": 1,
                "Immunity_Acquisition_Factor": 0,
                "Immunity_Transmission_Factor": 0,
                "Immunity_Initialization_Distribution_Type": "DISTRIBUTION_OFF",
                "Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY",
                "Transmission_Blocking_Immunity_Decay_Rate": 0.1,
                "Transmission_Blocking_Immunity_Duration_Before_Decay": 60
            },
            "MORTALITY": {
                "Base_Mortality": 0,
                "Enable_Disease_Mortality": 0,
                "Death_Rate_Dependence": "NONDISEASE_MORTALITY_BY_AGE_AND_GENDER",
                "Immunity_Mortality_Factor": 0,
                "Mortality_Blocking_Immunity_Decay_Rate": 0.001,
                "Mortality_Blocking_Immunity_Duration_Before_Decay": 60,
                "Mortality_Time_Course": "DAILY_MORTALITY"
            },
            "Population_Density_C50": 30,
            "Population_Scale_Type": "USE_INPUT_FILE",
            "SAMPLING": {
                "Base_Individual_Sample_Rate": 1,
                "Individual_Sampling_Type": "TRACK_ALL",
                "Max_Node_Population_Samples": 40,
                "Sample_Rate_0_18mo": 1,
                "Sample_Rate_10_14": 1,
                "Sample_Rate_15_19": 1,
                "Sample_Rate_18mo_4yr": 1,
                "Sample_Rate_20_Plus": 1,
                "Sample_Rate_5_9": 1,
                "Sample_Rate_Birth": 2
            }
        },
        "DISEASE": {
            "Animal_Reservoir_Type": "NO_ZOONOSIS",
            "Enable_Superinfection": 0,
            "INCUBATION": {
                "Base_Incubation_Period": 3,
                "Incubation_Period_Distribution": "FIXED_DURATION"
            },
            "INFECTIOUSNESS": {
                "Base_Infectious_Period": 7,
                "Base_Infectivity": 0.3,
                "Infectious_Period_Distribution": "EXPONENTIAL_DURATION",
                "Infectivity_Scale_Type": "CONSTANT_INFECTIVITY",
                "Population_Density_Infectivity_Correction": "CONSTANT_INFECTIVITY"
            },
            "Infection_Updates_Per_Timestep": 1,
            "Max_Individual_Infections": 1,
            "TRANSMISSION": {
                "Enable_Maternal_Infection_Transmission": 0,
                "Maternal_Transmission_Probability": 0
            }
        },
        "FUDGE_FACTORS": {
            "x_Air_Migration": 1,
            "x_Birth": 1,
            "x_Local_Migration": 1,
            "x_Other_Mortality": 1,
            "x_Population_Immunity": 1,
            "x_Regional_Migration": 1,
            "x_Sea_Migration": 1,
            "x_Temporary_Larval_Habitat": 1
        },
        "HPC": {
            "Job_Node_Groups": "Chassis08",
            "Job_Priority": "BELOWNORMAL",
            "Load_Balance_Filename": "",
            "Local_Simulation": 0,
            "Num_Cores": 1
        },
        "INTRANODE_TRANSMISSION": {
            "Enable_Default_Shedding_Function": 1,
            "Enable_Heterogeneous_Intranode_Transmission": 0
        },
        "MIGRATION": {
            "Migration_Model": "NO_MIGRATION"
        },
        "OUTPUT": {
            "Custom_Reports_Filename": "NoCustomReports",
            "Report_Event_Recorder": 0,
            "Enable_Default_Reporting": 1,
            "Enable_Property_Output": 0,
            "Enable_Spatial_Output": 0
        },
        "POLIO": {},
        "PRIMARY": {
            "Config_Name": "DEFAULT_CONFIG_NAME_SHOULD_BE_SET",
            "ENUMS": {
                "Simulation_Type": "GENERIC_SIM"
            },
            "Geography": "DEFAULT_GEOGRAPHY_SHOULD_BE_SET",
            "Node_Grid_Size": 0.042,
            "Run_Number": 0,
            "Simulation_Duration": 365,
            "Simulation_Timestep": 1,
            "Start_Time": 0,
            "Enable_Absolute_Time": "NO"

        },
        "SERIALIZATION": {
            "Burnin_Cache_Mode": "none",
            "Burnin_Cache_Period": 0,
            "Burnin_Name": "",
            "Serialization_Test_Cycles": 0
        },
        "STRAIN_TRACKING": {
            "Number_Basestrains": 1,
            "Number_Substrains": 1
        }
    }
}

Create the overlay configuration file in JSON. This file must include the parameter Default_Config_Path, set to the path to the default configuration file, relative to the location of the flatten_config.py script in the EMOD Regression folder. Again, you may organize the parameters into logical categories if you desire. See the example overlay configuration file below.

{
     "Default_Config_Path": "defaults/generic-default-config.json",
     "parameters": {
          "DEMOGRAPHICS": {
              "Enable_Demographics_Builtin": 0,
              "Birth_Rate_Dependence": "POPULATION_DEP_RATE",
              "Death_Rate_Dependence" : "NOT_INITIALIZED",
              "Enable_Vital_Dynamics": 0,
              "Sample_Rate_Birth": 1,
              "Enable_Demographics_Reporting": 1
          },
          "DISEASE": {
              "Base_Incubation_Period": 0,
              "Base_Infectious_Period": 4,
              "Base_Infectivity": 3.5,
              "Enable_Immune_Decay": 0
          },
          "PRIMARY": {
               "Config_Name": "00_Generic_DEFAULT",
               "Demographics_Filenames": ["demographics.json"],
               "Geography": "",
               "Run_Number": 1,
               "Simulation_Duration": 90
          }
     }
}

In a Command Prompt window, navigate to the Regression folder.
Run the flatten_config.py script, providing the relative path to the overlay file:
```
python flatten_config.py experiment/param_overlay.json
```

Open the resulting config.json file in the same folder as param_overlay.json and see that it has been flattened into a single layer with all parameters listed alphabetically and any logical categories removed. Eradication.exe will not accept a configuration file with nested JSON objects.

{
    "parameters": {
        "Acquisition_Blocking_Immunity_Decay_Rate": 0.1,
        "Acquisition_Blocking_Immunity_Duration_Before_Decay": 60,
        "Age_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE",
        "Animal_Reservoir_Type": "NO_ZOONOSIS",
        "Base_Incubation_Period": 0,
        "Base_Individual_Sample_Rate": 1,
        "Base_Infectious_Period": 4,
        "Base_Infectivity": 3.5,
        "Base_Mortality": 0,
        "Base_Population_Scale_Factor": 1,
        "Birth_Rate_Dependence": "POPULATION_DEP_RATE",
        "Birth_Rate_Time_Dependence": "NONE",
        "Burnin_Cache_Mode": "none",
        "Burnin_Cache_Period": 0,
        "Burnin_Name": "",
        "Campaign_Filename": "campaign.json",
        "Climate_Model": "CLIMATE_OFF",
        "Config_Name": "00_DEFAULT",
        "Custom_Reports_Filename": "NoCustomReports",
        "Death_Rate_Dependence": "NOT_INITIALIZED",
        "Default_Geography_Initial_Node_Population": 1000,
        "Default_Geography_Torus_Size": 10,
        "Demographics_Filenames": [
            "../00_Default/demographics.json"
        ],
        "Enable_Absolute_Time": "NO",
        "Enable_Aging": 1,
        "Enable_Birth": 1,
        "Enable_Default_Reporting": 1,
        "Enable_Default_Shedding_Function": 1,
        "Enable_Demographics_Birth": 0,
        "Enable_Demographics_Builtin": 0,
        "Enable_Demographics_Gender": 1,
        "Enable_Demographics_Risk": 0,
        "Enable_Demographics_Reporting": 1,
        "Enable_Disease_Mortality": 0,
        "Enable_Heterogeneous_Intranode_Transmission": 0,
        "Enable_Immune_Decay": 0,
        "Enable_Immunity": 1,
        "Enable_Interventions": 1,
        "Enable_Maternal_Infection_Transmission": 0,
        "Enable_Natural_Mortality": 0,
        "Enable_Property_Output": 0,
        "Enable_Spatial_Output": 0,
        "Enable_Superinfection": 0,
        "Enable_Vital_Dynamics": 0,
        "Geography": "",
        "Immunity_Acquisition_Factor": 0,
        "Immunity_Initialization_Distribution_Type": "DISTRIBUTION_OFF",
        "Immunity_Mortality_Factor": 0,
        "Immunity_Transmission_Factor": 0,
        "Incubation_Period_Distribution": "FIXED_DURATION",
        "Individual_Sampling_Type": "TRACK_ALL",
        "Infection_Updates_Per_Timestep": 1,
        "Infectious_Period_Distribution": "EXPONENTIAL_DURATION",
        "Infectivity_Scale_Type": "CONSTANT_INFECTIVITY",
        "Job_Node_Groups": "Chassis08",
        "Job_Priority": "BELOWNORMAL",
        "Listed_Events": [],
        "Load_Balance_Filename": "",
        "Local_Simulation": 0,
        "Maternal_Transmission_Probability": 0,
        "Max_Individual_Infections": 1,
        "Max_Node_Population_Samples": 40,
        "Migration_Model": "NO_MIGRATION",
        "Minimum_Adult_Age_Years": 15,
        "Mortality_Blocking_Immunity_Decay_Rate": 0.001,
        "Mortality_Blocking_Immunity_Duration_Before_Decay": 60,
        "Mortality_Time_Course": "DAILY_MORTALITY",
        "Node_Grid_Size": 0.042,
        "Num_Cores": 1,
        "Number_Basestrains": 1,
        "Number_Substrains": 1,
        "PKPD_Model": "FIXED_DURATION_CONSTANT_EFFECT",
        "Population_Density_C50": 30,
        "Population_Density_Infectivity_Correction": "CONSTANT_INFECTIVITY",
        "Population_Scale_Type": "USE_INPUT_FILE",
        "Report_Event_Recorder": 0,
        "Run_Number": 1,
        "Sample_Rate_0_18mo": 1,
        "Sample_Rate_10_14": 1,
        "Sample_Rate_15_19": 1,
        "Sample_Rate_18mo_4yr": 1,
        "Sample_Rate_20_Plus": 1,
        "Sample_Rate_5_9": 1,
        "Sample_Rate_Birth": 1,
        "Serialization_Test_Cycles": 0,
        "Simulation_Duration": 90,
        "Simulation_Timestep": 1,
        "Simulation_Type": "GENERIC_SIM",
        "Start_Time": 0,
        "Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY",
        "Transmission_Blocking_Immunity_Decay_Rate": 0.1,
        "Transmission_Blocking_Immunity_Duration_Before_Decay": 60,
        "x_Air_Migration": 1,
        "x_Birth": 1,
        "x_Local_Migration": 1,
        "x_Other_Mortality": 1,
        "x_Population_Immunity": 1,
        "x_Regional_Migration": 1,
        "x_Sea_Migration": 1,
        "x_Temporary_Larval_Habitat": 1
    }
}

Note

These same steps can also be used to flatted a single configuration file that has been hierarchically organized into logical categories.

Create a campaign file¶

You define the initial disease outbreak and interventions used a campaign for a simulation through a JSON-formatted campaign file, typically called campaign.json. It is hierarchically organized into logical groups of parameters that can have arbitrary levels of nesting. For some interventions, there can be a very complex hierarchical structure, including recursion. This framework enables rigorous testing of possible control strategies to determine which events or combination of events will best aid in the elimination of disease for specific geographic locations.

For a complete list of campaign parameters that are available to use with this simulation type and more detail about the campaign file structure, see Campaign parameters. For more information about JSON, see EMOD parameter reference.

For the interventions to take place, the campaign file must be in the same directory as the configuration file and you must set the configuration parameters Enable_Interventions to 1 and Campaign_Filename to the name of the campaign file.

The EMOD Regression directory contains many different subdirectories that contain configuration, campaign, and other associated files to run simulations that represent real-world scenarios. Within the each subdirectory, there is usually a single campaign file (campaign.json), though some directories also include a campaign overlay file (campaign_overrides.json), which has been created by combining campaign_overrides.json with one of the default files in Regression/defaults. The naming of these files is an arbitrary convention used at IDM; you may name this files anything you choose. See Use campaign overlay files for more information flattening two campaign files.

Although you can create campaign files entirely from scratch, it is often easier to start from an existing campaign file and modify it to meet your needs. The simplest method is to edit the parameters or parameter values in the JSON file in any text editor. You may also create or modify the files using a scripting language, as with configuration files. See Create a configuration file for examples.

The following is an example of campaign file that has two events (SimpleVaccine and Outbreak) that occur in all nodes at day 1 and day 30, respectively. Each event contains an event coordinator that describes who receives the intervention (everyone, with the vaccine repeated three times) and the configuration for the intervention itself. Note that the nested JSON elements have been organized to best illustrate their hierarchy, but that many files in the Regression directory list the parameters and nested objects differently.

{
    "Campaign_Name": "Vaccine",
    "Use_Defaults": 1,
    "Events":
    [
        {
            "Event_Name": "SimpleVaccine",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 1,
            "class": "CampaignEvent",
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.5,
                "Number_Repetitions": 3,
                "Target_Demographic": "Everyone",
                "Timesteps_Between_Repetitions": 7,
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "Cost_To_Consumer": 10,
                    "Waning_Config": {
                        "class": "WaningEffectMapLinear",
                        "Initial_Effect" : 1.0,
                        "Expire_At_Durability_Map_End" : 0,
                        "Durability_Map" : {
                            "Times"  : [   0,  30,  60,  90, 120 ],
                            "Values" : [ 0.9, 0.3, 0.9, 0.6, 1.0 ]
                        }
                    },
                    "Vaccine_Take": 1,
                    "Vaccine_Type": "AcquisitionBlocking",
                    "class": "SimpleVaccine"
                }
            },
        },
        {
            "Event_Name": "Outbreak",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 30,
            "class": "CampaignEvent",
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.001,
                "Target_Demographic": "Everyone",
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "Antigen": 0,
                    "Genome": 0,
                    "Outbreak_Source": "PrevalenceIncrease",
                    "class": "OutbreakIndividual"
                }
            }
        }
    ]
}

Multiple interventions¶

When creating multiple interventions, either of the same type or different types, they will generally be distributed independently without regard to whether a person has already received another intervention.

Warning

The event containing the outbreak “intervention” must be the last one listed in the campaign file or none of the disease control interventions will take place.

For example, say you create two SimpleBednet interventions and both interventions have Demographic_Coverage set to 0.5 (50% demographic coverage). This value is the probability that each individual in the target population will receive the intervention. It does not guarantee that the exact fraction of the target population set by Demographic_Coverage receives the intervention.

By default, each individual in the simulation will have a 50% chance of receiving a bednet in both of the distributions and the two distributions will be independent. Therefore, each individual has a 75% chance of receiving at least one bednet.

Use campaign overlay files¶

You can use two campaign files when setting up a simulation. One file contains default campaign settings and an overlay file contains additional parameters or different parameter values that override the values in the default file. This topic describes how to set up the default and overlay file and flatten them into a single file before running a simulation.

Overlay files allow you to easily separate a subset of parameters that are of particular interest from the rest of the parameters needed to run a simulation. You can easily modify the parameters in the overlay file without needing to maintain a complete campaign file. This can be especially helpful when you want to experiment with different interventions without modifying the rest of the settings. You can have one default file and many different overlay files for different intervention settings. It also allows you to easily update the default values across multiple simulations.

In addition to being used for model experimentation, overlay files are used when testing the software functionality after making source code changes. If you run the EMOD regression tests using regression_test.py, campaign files will be flattened as part of those tests. However, this may take several hours if run locally. More guidance on modifying the EMOD source code is in the “Advance the model” section.

Follow the steps below to quickly flatten campaign files.

Create the default campaign file in JSON. You may, though it is not required, organize the parameters into logical categories of nested JSON objects to make managing the parameters easier. See Campaign parameters for a complete list of all parameters that are available. See the example default campaign file below.

{
    "Campaign_Name": "Initial Seeding",
    "Use_Defaults": 1,
    "Events": [
        {
            "Event_Name": "Outbreak",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 30,
            "class": "CampaignEvent",
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.001,
                "Target_Demographic": "Everyone",
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "Antigen": 0,
                    "Genome": 0,
                    "Outbreak_Source": "PrevalenceIncrease",
                    "class": "OutbreakIndividual"
                }
            }
        }
    ]
}

Create the overlay campaign file in JSON. This file must include the parameter Default_Campaign_Path, set to the path to the default campaign file, relative to the location of the flatten_campaign.py script in the EMOD Regression folder. Again, you may organize these into logical categories if you desire. See the example overlay campaign file below.

{
    "Campaign_Name": "Vaccine",
    "Default_Campaign_Path": "defaults/generic-default-campaign.json",
    "Events": [
        {
            "VACCINATION": "BEGIN",
            "Event_Name": "SimpleVaccine",
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.5,
                "Intervention_Config": {
                    "Cost_To_Consumer": 10,
                    "Waning_Config": {
                        "class": "WaningEffectMapLinear",
                        "Initial_Effect" : 1.0,
                        "Expire_At_Durability_Map_End" : 0,
                        "Durability_Map" :
                        {
                            "Times"  : [   0,  30,  60,  90, 120 ],
                            "Values" : [ 0.9, 0.3, 0.9, 0.6, 1.0 ]
                        }
                    },
                    "Vaccine_Take": 1,
                    "Vaccine_Type": "AcquisitionBlocking",
                    "class": "SimpleVaccine"
                },
                "Number_Repetitions": 3,
                "Target_Demographic": "Everyone",
                "Timesteps_Between_Repetitions": 7,
                "class": "StandardInterventionDistributionEventCoordinator"
            },
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 1,
            "class": "CampaignEvent",
            "VACCINATION": "END"
        }
    ]
}

In a Command Prompt window, navigate to the Regression folder.
Run the flatten_campaign.py script, providing the relative path to the overlay file and the path to and name of the new flattened campaign file that will be saved, using the arguments as shown below:
```
python flatten_campaign.py --overlay experiment/campaign_overlay.json --saveto experiment/campaign.json
```

Open the resulting campaign.json file and see that it has been flattened into a single file with nested JSON objects and any logical categories retained.

{
    "Campaign_Name": "Vaccine",
    "Default_Campaign_Path": "defaults/generic-default-campaign.json",
    "Use_Defaults": 1,
    "Events":
    [
        {
            "VACCINATION": "BEGIN",
            "Event_Name": "SimpleVaccine",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 1,
            "class": "CampaignEvent",
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.5,
                "Number_Repetitions": 3,
                "Target_Demographic": "Everyone",
                "Timesteps_Between_Repetitions": 7,
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "Cost_To_Consumer": 10,
                    "Waning_Config": {
                        "class": "WaningEffectMapLinear",
                        "Initial_Effect" : 1.0,
                        "Expire_At_Durability_Map_End" : 0,
                        "Durability_Map" : {
                            "Times"  : [   0,  30,  60,  90, 120 ],
                            "Values" : [ 0.9, 0.3, 0.9, 0.6, 1.0 ]
                        }
                    },
                    "Vaccine_Take": 1,
                    "Vaccine_Type": "AcquisitionBlocking",
                    "class": "SimpleVaccine"
                }
            },
            "VACCINATION": "END"
        },
        {
            "Event_Name": "Outbreak",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 30,
            "class": "CampaignEvent",
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.001,
                "Target_Demographic": "Everyone",
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "Antigen": 0,
                    "Genome": 0,
                    "Outbreak_Source": "PrevalenceIncrease",
                    "class": "OutbreakIndividual"
                }
            }
        }
    ]
}

After the overlay files and default files are combined into a single campaign file, you can run a simulation using the EMOD executable (Eradication.exe).

Target interventions to nodes or individuals¶

Generally, you want to target your outbreaks and campaign interventions to specific regions or individuals who meet certain criteria. For example, you may want to distribute bednets only to areas where a mosquito-borne disease is endemic or vaccinate only young children who are at highest risk. This topic describes how to distribute interventions to specific geographic nodes or groups of individuals.

Target to nodes using Nodeset_Config¶

Targeting geographic nodes with a particular intervention can be controlled by the Nodeset_Config parameter in the campaign file. To distribute the intervention to all nodes, simply set it to “NodeSetAll”. To distribute to a subset of nodes, follow the steps below.

In the JSON object for the campaign event, set Nodeset_Config using one of the following two options:
- Set it to an empty JSON object. Within that object, set the following:
  - Set class to “NodeSetNodeList”.
  - Set Node_List to an array that contains a comma-delimited list of nodes that set where the intervention will be distributed.
- Set it to an empty JSON object. Within that object, set the following:
  - Set class to “NodeSetPolygon”.
  - Set Polygon_Format to “SHAPE”.
  - Set Vertices to a comma-delimited list of latitude and longitude pairs that define the outer boundary of the region you want to target. The intervention will be distributed to all nodes within the defined bounds.

See the example below.

{
    "Use_Defaults": 1,
    "Events": [{
        "Event_Name": "Outbreak",
        "Nodeset_Config": {
            "class": "NodeSetNodeList",
            "Node_List": [1, 3, 5]
        },
        "Start_Day": 10,
        "class": "CampaignEvent"
    }]
}

Target to nodes or individuals using properties¶

To target interventions to particular individuals or nodes based on their property values, you must first define those properties in the demographics file using IndividualProperties or NodeProperties. Then, in the campaign file, you can target an intervention or outbreak to a group of individuals based on the properties applied to them. See Configure heterogeneity using individual and node properties for instructions on defining properties.

Just as defining properties based on age works a little differently than other properties, targeting an intervention to a particular age range works a little differently than targeting an intervention to other properties.

To target interventions to properties other than age:

In the campaign file, in the event you want to target, set Target_Demographic to “Everyone”.
Add the Property_Restrictions parameter and set to an empty array.
In that array, add a list of JSON key-value pairs of the property type and value that specifies the groups to apply the intervention to. If the name of the element is not valid, EMOD will ignore the property restriction.

To target interventions to age ranges:

In the campaign file, in the event you want to target, set Target_Demographic to ExplicitAgeRanges.
Add the Target_Age_Min and set to the lower age bound.
Add the Target_Age_Max and set to the upper age bound.

Both of these values must match one the values listed in Age_Bin_Edges_In_Years in the demographics file. EMOD does not verify this range.

The following examples illustrate how to target interventions to different groups. This includes how to configure interventions when there are multiple relevant properties, such as targeting individuals who are both low-risk and in a suburban setting or individuals who are either low- risk or living in suburban settings.

Single property, single value¶

The following examples show how to target interventions based on a single property value.

At day 0 of the simulation, an outbreak starts. Target_Demographic is set to “Everyone” but Property_Restrictions restricts the start of the outbreak to the “Urban” group.

{
    "Events": [{
        "Event_Coordinator_Config": {
            "Number_Distributions": -1,
            "Intervention_Config": {
                "Antigen": 0,
                "Genome": 0,
                "Outbreak_Source": "PrevalenceIncrease",
                "class": "OutbreakIndividual"
            },
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Property_Restrictions": [
                "Place:Urban"
            ],
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 0,
        "class": "CampaignEvent"
    }]
}

Even if you have multiple properties defined in the demographics file, you can target interventions to a single property value in the same way. Individuals can be assigned any of the values for the other property types.

In this example, values are defined for both the “Risk” and “Place” property types. The outbreak only targets “Suburban” individuals using the “Place” property type. Individuals can have any “Risk” value.

{
    "Events": [{
        "Event_Coordinator_Config": {
            "Number_Distributions": -1,
            "Intervention_Config": {
                "Antigen": 0,
                "Genome": 0,
                "Outbreak_Source": "PrevalenceIncrease",
                "class": "OutbreakIndividual"
            },
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Property_Restrictions": [
                "Place:Urban"
            ],
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 0,
        "class": "CampaignEvent"
    }]
}

Single property, multiple values¶

If you want to target multiple values with the same property type, such as both “Urban” and “Rural” values with the “Place” property, you must use multiple interventions. You cannot have more than one group with the same property value in one intervention or outbreak.

In this example, an outbreak starts at day 0 in the both the “Rural” and “Urban” groups.

{
    "Events": [{
        "Event_Coordinator_Config": {
            "Number_Distributions": -1,
            "Intervention_Config": {
                "Antigen": 0,
                "Genome": 0,
                "Outbreak_Source": "PrevalenceIncrease",
                "class": "OutbreakIndividual"
            },
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Property_Restrictions": [
                "Place:Rural"
            ],
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 0,
        "class": "CampaignEvent"
    }, {
        "Event_Coordinator_Config": {
            "Number_Distributions": -1,
            "Intervention_Config": {
                "Antigen": 0,
                "Genome": 0,
                "Outbreak_Source": "PrevalenceIncrease",
                "class": "OutbreakIndividual"
            },
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Property_Restrictions": [
                "Place:Urban"
            ],
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 0,
        "class": "CampaignEvent"
    }]
}

Multiple properties, individuals must match all values¶

To target individuals how have values defined by different property types, such as people who are both urban and low risk, you can use a single intervention or outbreak. When two values from multiple properties are targeted in one intervention or outbreak, the event is only applied to individuals that are assigned both values.

In this example, a vaccine intervention is targeted at low risk, suburban place individuals. Individuals who are targeted to receive the vaccine must have both property values.

{
    "Events": [{
        "Event_Coordinator_Config": {
            "Demographic_Coverage": 1.0,
            "Intervention_Config": {
                "Cost_To_Consumer": 10,
                "Durability_Time_Profile": "BOXDECAYDURABILITY",
                "Primary_Decay_Time_Constant": 3650,
                "Reduced_Acquire": 1,
                "Reduced_Transmit": 0,
                "Secondary_Decay_Time_Constant": 3650,
                "Vaccine_Take": 1,
                "Vaccine_Type": "StrainSpecific",
                "class": "SimpleImmunoglobulin"
            },
            "Target_Demographic": "Everyone",
            "Property_Restrictions": [
                "Risk:Low",
                "Place:Suburban"
            ],
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 100,
        "class": "CampaignEvent"
    }]
}

Multiple properties, individuals must match at least one value¶

However, if you want to target multiple properties, but individuals need to have only one of the specified values to qualify for the intervention, you must create a separate campaign event for each of the targeted property values. The events are applied separately and are applied to individuals that have one OR both property values; they need not have both.

In this example, an outbreak is targeted at low risk individuals in the first intervention and suburban individuals in the second intervention.

{
    "Events": [{
        "Event_Coordinator_Config": {
            "Number_Distributions": -1,
            "Intervention_Config": {
                "Antigen": 0,
                "Genome": 0,
                "Outbreak_Source": "PrevalenceIncrease",
                "class": "OutbreakIndividual"
            },
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Property_Restrictions": [
                "Risk:Low"
            ],
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 0,
        "class": "CampaignEvent"
    }, {
        "Event_Coordinator_Config": {
            "Number_Distributions": -1,
            "Intervention_Config": {
                "Antigen": 0,
                "Genome": 0,
                "Outbreak_Source": "PrevalenceIncrease",
                "class": "OutbreakIndividual"
            },
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Property_Restrictions": [
                "Place:Suburban"
            ],
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 0,
        "class": "CampaignEvent"
    }]
}

Alternatively, if you want to target multiple properties, one of which is the age bin, you could use Property_Restriction_Within_Node and the Age_Bin properties and Age_Bin_Property_From_X_To_Y values that are automatically created by EMOD when you use the Age_Bin_Edges_In_Years demographics parameter (see NodeProperties and IndividualProperties parameters). This allows you to use a single campaign event instead of multiple ones as you would with “Target_Demographic”: “ExplicitAgeRanges”.

{
    "Events": {
        "class": "CampaignEvent",
        "Distributions": [{
            "Start_Day": 1,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "EventCoordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Demographic_Coverage": 1,
                "Intervention_Config": {
                    "class": "NodeLevelHealthTriggeredIV",
                    "Property_Restrictions_Within_Node": [{
                            "Risk": "LOW",
                            "Age_Bin": "Age_Bin_Property_From_0_To_5"
                        },
                        {
                            "Risk": "MEDIUM",
                            "Age_Bin": "Age_Bin_Property_From_5_To_13"
                        },
                        {
                            "Risk": "HIGH",
                            "Age_Bin": "Age_Bin_Property_From_13_To_125"
                        }
                    ]
                }
            }
        }]
    }
}

Target an age range¶

Targeting an intervention to an age range is set up differently than targeting an intervention to other property types. However, you can combine both kinds of restrictions. In this example, a vaccine campaign is targeted at urban individuals who are age 0 to 5.

{
    "Events": [{
        "Event_Coordinator_Config": {
            "Demographic_Coverage": 1.0,
            "Intervention_Config": {
                "Cost_To_Consumer": 10,
                "Durability_Time_Profile": "BOXDECAYDURABILITY",
                "Primary_Decay_Time_Constant": 3650,
                "Reduced_Acquire": 1,
                "Reduced_Transmit": 0,
                "Secondary_Decay_Time_Constant": 3650,
                "Vaccine_Take": 1,
                "Vaccine_Type": "StrainSpecific",
                "class": "SimpleImmunoglobulin"
            },
            "Target_Demographic": "ExplicitAgeRanges",
            "Target_Age_Min": 0,
            "Target_Age_Max": 5,
            "Property_Restrictions": [
                "Place:Urban"
            ],
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 30,
        "class": "CampaignEvent"
    }]
}

Run simulations¶

There are a number of ways to run an EMOD simulation, whether locally or on a remote HPC cluster. The simplest way is to run a simulation at the command line, however that limits you to running one local simulation at a time. Because the EMOD model is stochastic, you must run many simulations before you can interpret the outcome. You may want to use a scripting language like Python or MATLAB or an application like mpiexec to run multiple simulations at once.

In addition, if you are modifying the EMOD source code to add functionality to the model, you can run simulations in Visual Studio as part of debugging. This process is described in Run debug simulations in Visual Studio.

No matter how you choose to run simulations, you must have a built copy of the EMOD executable (Eradication.exe) or Eradication binary, either downloaded directly from GitHub or built from the EMOD source code. See EMOD installation. In addition, you must know the paths to the configuration file and input data files and where you want to store the output files. You will pass this information as arguments to Eradication.exe.

Directory structure¶

Although there are many ways you can structure the files needed to run a simulation, we recommend the following to keep your files organized and simplify the file paths set in the configuration file or passed as arguments to Eradication.exe.

Place the configuration and campaign files needed for a simulation in the same directory. This is also known as the working directory.

However, if you are using overlay files, you may want the default configuration or campaign file in a separate directory so they can be used with different overlay files for other simulations.
Place all input data files for a given region in the same directory.
Place output for a simulation in a subdirectory of the directory containing configuration and campaign files.

It is not important where you install Eradication.exe or the Eradication binary.

Run a simulation using the command line¶

Using command-line options is the simplest way to run an EMOD simulation. This topic describes the commands available for running simulations.

The EMOD executable (Eradication.exe) and Eradication binary also provide commands that provide information about the version of EMOD that is installed, such as available parameters and simulation types. For more information, see Generate a list of all available parameters (a schema). The examples below show the Windows Eradication.exe, but the options are the same for the Eradication binary on CentOS on Azure.

Command options¶

The following command-line options are available for running EMOD simulations. Paths can be absolute or relative to the directory you are in when running the commands, unless otherwise specified.

Simulation options¶
Long form	Short form	Description
`--config`	`-C`	Path to the configuration file. If not specified, EMOD will look for a file named default-config.json in the current directory.
`--input-path`	`-I`	Path to the directory containing input data files. If not specified, EMOD will look for files in the current directory.
`--output-path`	`-O`	Path to the directory where output files will be written. If not specified, EMOD will create an “output” directory and overwrite any previous output in that directory.
`--dll-path`	`-D`	Path to the EMODule root directory. For more information, see Custom reporters.

The following options are for monitoring the progress of simulations running on an high-performance computing (HPC) cluster. They are optional for any simulation, but they must be used together. To monitor progress, listen for User Datagram Protocol (UDP) messages on the specified host and port.

HPC cluster options¶
Long form	Description
`--monitor_host`	The IP address of the commissioning/monitoring host. Set to “none” for no IP address.
`--monitor_port`	The port of the commissioning/monitoring host. Set to “0” for no port.
`--sim_id`	The unique ID for this simulation. This ID is needed for self-identification to the UDP host. Set to “none” for no simulation ID.

Usage¶

Open a Command Prompt window and navigate to the working directory, which contains the configuration and campaign files.
Enter a command like the one illustrated below, substituting the appropriate paths and file names for your simulation:
```
../Eradication.exe -C config.json -I C:\EMOD\InputFiles -O Sim1Output
```
If you do not specify anything when invoking Eradication.exe, it will not execute with all defaults, but will instead tell you how to invoke the --help command.
EMOD will display logging information, including an errors that occur, while running the simulation. See Error and logging files for more information.

Run a simulation using mpiexec¶

The application mpiexec is used to run multi-node simulations in parallel. Eradication.exe is “single threaded”, so it uses only one core for processing. If you run a simulation with multiple geographic nodes using mpiexec instead of invoking Eradication.exe directly, multiple copies of Eradication.exe will be running, with one copy per core processing data for a single node at a time. Message Passing Interface (MPI) communicates between the cores when handling the migration of individuals from one node to another.

Although mpiexec can be used to run a simulation in parallel on your local computer, it is more often used to run complex simulations in parallel on an HPC cluster or several linked computers. Mpiexec is part of the Microsoft HPC Pack 2012 SDK (64-bit) installed as a prerequisite for building Eradication.exe from the EMOD source code. See EMOD source code installation for more information.

Note

If you get an error that the mpiexec command cannot be found, you must add the path to mpiexec to the PATH environment variable. For example, open Control Panel and add the path C:\Program Files\Microsoft HPC Pack 2012\Bin to PATH.

Usage¶

Take note of the number of cores on your computer or cluster.

If running locally, we recommend running mpiexec with one fewer cores than are available, so one core is reserved for the operating system. The simulation can be run on all available cores and will complete faster, but the desktop will not be responsive.
Open a Command Prompt window and navigate to the directory that contains the configuration and campaign files for the simulation.
Invoke Eradication.exe using mpiexec as follows, replacing the number of cores, paths, and command options as necessary for your environment. See Run a simulation using the command line for more information about the command options available for use with Eradication.exe.
```
mpiexec -n 3 ..\Eradication.exe --config config.json --input-path ..\InputDirectory\Garki --output-path OutputGarki
```

Mpiexec will start multiple copies of Eradication.exe as specified by -n. Those instances will communicate with each other via MPI. If all cores are on a single computational node or host, they will use internal networking to carry the MPI packets.

Note

The EMOD executable (Eradication.exe) does NOT use the configuration file parameter Num_Cores, which is used by the infrastructure that runs the simulation, such as an HPC cluster or regression script.

You can also link together several computers with MPI using the mpiexec -host option. For example, if you were using six cores on two computers, you could run three copies of Eradication.exe on the first computer, and three more could be run on the second computer. Again, this assumes that each computer has at least three cores.

For more information about mpiexec, see MSDN.

Run a simulation using Python¶

If you used Python to create or modify JSON files as shown in Create a configuration file or Create a campaign file, it may be convenient to invoke Eradication.exe to run a simulation from a Python script. One way of doing this is shown below using the subprocess package.

import subprocess

# specify paths
binary_path = "binDirectory\Eradication.exe"
input_path = "inputDirectory\Namawala\

# commission job
subprocess.call( [binary_path, "-C", "config.json", "--input", input_path] )

See Run a simulation using the command line for more information about the command options available for use with Eradication.exe.

Run a simulation using MATLAB¶

If you used MATLAB to create or modify JSON files as shown in Create a configuration file or Create a campaign file, it may be convenient to invoke Eradication.exe to run a simulation from a MATLAB script. One way of doing this is shown below using the dos command.

exe_name = fullfile( binDirectory, 'Eradication.exe' );
exe_args = [ '-C config.json -I ', fullfile( inputDirectory, 'Namawala' ) ];
[status,result] = dos( ['cd ', WorkDirectory, ' && ', exe_name, ' ', exe_args ], '-echo' );

See Run a simulation using the command line for more information about the command options available for use with Eradication.exe.

Improve EMOD performance¶

While small simulations can be run quickly on a local computer, the time and memory needed to complete a simulation can grow significantly when simulations become larger and more complex. For example, a single geographical node simulation might use 3 GB with one core in order to run successfully in one minute or less, while another simulation job may require 32 GB in a dual-core system in order to complete in approximately the same amount of time.

As your simulations grow, you will likely want to run simulations on an HPC cluster or take other steps to improve performance and reduce processing time. This topic describes many of the steps you can take to speed up EMOD simulations.

Parallel processing¶

The EMOD executable (Eradication.exe) is “single threaded”, meaning that for processing, it will use only one core. However, you can use the Message Passing Interface (MPI) to run multiple copies of Eradication.exe in parallel, either locally or on an HPC cluster. To run a simulation in parallel, you must invoke Eradication.exe with the mpiexec command. For more information, see Run a simulation using mpiexec.

Parameter settings¶

You can also set various parameters in the configuration file that will improve performance by scaling down the amount of data used or optimizing data processing. See the parameters listed in the sections below for guidance on making performance adjustments. See Configuration parameters for more information about each of the parameters.

Scaling and sampling¶

Simulation_Duration: Obviously, simulating a shorter timespan will take less processing time. However, the processing time is often driven by the number of infections or immune updates, so running a simulation after all infections have cleared may not increase processing time much. For more information, see Simulation setup parameters.
Individual_Sampling_Type and Base_Individual_Sample_Rate: Instead of the default Individual_Sampling_Type setting of “TRACK_ALL”, you can speed up performance by sampling such that each individual object represents more than one person. For example, a simulation with a population of 1 million and sample rate of 0.1 would simulate 100,000 individuals, with each given a weight of 10.Sampling can be fixed at a particular rate or can adapt the rate based on certain criteria, such as age or immune state. However, you should be especially careful not to undersample simulations to the point where they are overly sensitive to rare stochastic events. For more information, see Sampling parameters.
Population_Scale_Type and Base_Population_Scale_Factor: Alternatively, you can simply reduce the total population of the simulation using Population_Scale_Type set to “FIXED_SCALING” and Base_Population_Scale_Factor set to less than one. For more information, see Scalars and multipliers parameters.

Processing¶

Num_Cores: For large, spatially distributed simulations, running the intra-node dynamics (for example, infection and immune dynamics) in parallel on multiple cores may be very advantageous. Ideally, the timing would be reduced inversely to the number of cores. However, there are costs to serializing individuals for migration over MPI, as well as considerations for balancing the CPU load on each core. These issues may be mitigated using a load balancing input file that is suitable to the geography being simulated. See Load-balancing file structure for more information.

Use output files¶

At the end of the simulation, you will notice that a number of files have been written to the output directory. Some are logging or error output files, which you can read about more in Error and logging files. The rest are output reports that contain data from the simulation itself, usually in JSON or CSV format. This topic describes how to parse and use the output reports.

By default, the output report InsetChart.json is always produced, which contains per- timestep values accumulated over the simulation in a variety of reporting channels, for example, “New Infections”, “Adult Vectors”, and “Parasite Prevalence”. EMOD provides several other built-in reports that you can produce if you enable them in the configuration file. See Output report structure for additional information about the reports and how to enable them.

If none of the built-in output reports provide the data you need, you can use a custom reporter that plugs in to the Eradication.exe as a EMODule dynamic link library (DLL). For more information, see Custom reporters.

In order to interpret the output of EMOD simulations, you will find it useful to parse the output reports into an analyzable structure. For example, you can use a Python or MATLAB script to create graphs and charts for analysis.

Use Python to parse data¶

The example below uses the Python package JSON to parse the file and the Python package matplotlib.pyplot to plot the output. This is a very simple example and not likely the most robust or elegant. Be sure to set the actual path to your working directory.

import os
import json
import matplotlib.pyplot as plt

# open and parse InsetChart.json
ic_json = json.loads( open( os.path.join( WorkingDirectoryLocation, "output", "InsetChart.json" ) ).read() )
ic_json_allchannels = ic_json["Channels"]
ic_json_birthdata = ic_json["Channels"]["Births"]

# plot "Births" channel by time step
plt.plot( ic_json_birthdata[  "Data"  ], 'b-' )
plt.title( "Births" )
plt.show()

Use MATLAB to parse data¶

The example below uses the MATLAB toolbox JSONlab to parse an InsetChart.json file and plot one channel. This script uses JSONLab to parse the file into a usable form in MATLAB. This is a very simple example and not likely the most robust or elegant. Be sure to set the actual paths to JSONlab and your working directory.

% this sample uses JSONLab toolbox
addpath('PATH TO/jsonlab');

% open and parse InsetChart.json
ic_json = loadjson( fullfile( 'WorkingDirectoryLocation', 'output', 'InsetChart.json' ));
ic_json_allchannels = ic_json.Channels;
ic_json_birthinfo = ic_json_allchannels.Births;
ic_json_birthdata = ic_json_birthinfo.Data;
M = num2cell(ic_json_birthdata);

% plot "Births" channel by time step
plot(cell2mat(M));
title( 'Births' );

Custom reporters¶

Reporters extract simulation data, aggregate it, and output it to a file known as an output report. You can process the report data using tools, such as Python or MATLAB, to create graphs and charts for analysis. EMOD provides built-in reporters that are part of the EMOD executable (Eradication.exe) and can be enabled or disabled by setting parameters in the configuration file. See Output report structure for a list of the reports that are available using built-in reporters.

In addition, you can use custom reporters that extract data from the simulation and aren’t part of the Eradication.exe. A custom reporter is an EMODule that you plug in to EMOD. Custom reporters are not supported for CentOS on Azure. There are several reporters in the GitHub reporters directory that you can use. You may also want to build your own custom reporter to create a new output report.

The Eradication.exe must load the reporter when running a simulation to use it. If it is loaded, the output report will be automatically generated at the end of the simulation. There are three ways to specify their location so Eradication.exe can load them. Eradication.exe attempts to load the file using the following methods, in the order listed:

Define the location of the dynamic link library (DLL) in a JSON-formatted file (emodules_map.json).
Point to the location using the --dll-path command-line option when invoking Eradication.exe.
Place the DLL in the working directory.

JSON file¶

First, Eradication.exe looks for an emodules_map.json file in the working directory. This map file lists specific reporter DLLs and their exact location. This is useful if you want to store all custom reporters in the same directory, but only want certain reporters to be used with each simulation.

The emodules_map.json file has three sections: diseases, interventions and reporters. Each section is a JSON array and contains the path to each EMODule in the array. Although reporter EMODules are the only type currently supported, empty disease and intervention sections must be included. You must use either a double backslash (\\) or a single forward slash (/) to represent a single backslash for the path. For example:

{
    "disease_plugins": [
         ],
    "interventions": [
         ],
    "reporter_plugins": [
        "C:\\src\\EMOD\\x64\\Release\\emodules\\reporter_plugins\\libreportpluginbasic.dll"
    ]
}

Command-line option¶

If a map file is not provided, you can include the --dll-path option when you invoke Eradication.exe. It must point to a directory with a reporter_plugins subdirectory that contains the reporters. The name of the directory does not matter, but the reporter must be in a subdirectory named reporter_plugins. Note that this method will load all reporters in the root directory specified.

If you build custom reporters as described here, the DLLs will be saved to the following directories, depending on the build configuration:

<EMOD source directory path>x64Releasereporter_plugins
<EMOD source directory path>x64Debugreporter_plugins

Therefore, if you do not move your reports, you will set --dll-path to one of the following paths:

<EMOD source directory path>x64Release
<EMOD source directory path>x64Debug

For more information on EMOD command-line options, see Run a simulation using the command line.

Working directory¶

Finally, if neither the the map file or the command-line option are provided, Eradication.exe looks for the reporter plug-in in the current working directory.

EMOD parameter reference¶

The configurable parameters described in this reference section determine the behavior of a particular simulation and interventions. The parameter descriptions are intended to guide you in correctly configuring simulations to match your disease scenario of interest.

This reference section contains only the parameters supported for use with the tuberculosis simulation type. To see the parameters for a different simulation type, see the corresponding documentation.

These parameters include three major groups: parameters for the demographics file, parameters for the configuration file, and parameters for the campaign file.

If you would rather use a text file for parameter reference than this web documentation, you can also generate a schema with the EMOD executable (Eradication.exe) that defines all configuration and campaign parameters that can be used in a simulation, including names, data types, defaults, ranges, and short descriptions. For instructions, see Generate a list of all available parameters (a schema).

JSON formatting overview¶

All of these parameters are contained in JavaScript Object Notation (JSON) formatted files. JSON is a data format that is human-readable and easy for software to read and generate. It can be created and edited using a simple text editor.

JSON has two basic structures: a collection of key-value pairs or an ordered list of values (an array). These structures are within a JSON object, which is enclosed in a set of braces. You will notice that each of these files begins with a left brace ({) and ends with a right brace (}).

A value can be a string, number, Boolean, array, or an object. The campaign and data input files often use nested objects and arrays. See www.json.org for more information on JSON.

A few important details to call out when creating JSON files:

Add a comma after every key-value pair or array except for the last key-value pair in an array or object.
The keys (parameters) are case-sensitive. For example, “NodeID” is not the same as “NodeId”.
Decimals require a 0 (zero) before the decimal point.
EMOD does not support Booleans (“True”, “False”). Instead, EMOD use the integer 1 for “True” and 0 for “False”.
Every opening brace or bracket ({ or [) requires a corresponding closing brace or bracket (} or ]).

The following is an example of a JSON formatted file.

{
    "A_Complex_Key": {
        "An_Array_with_a_Nested_Object_Value": [
            {
                "A_Simple_Key": "Value",
                "A_Simple_Array": [ "Value1", "Value2" ],
                "An_Array_with_Number_Values": [ 0.1, 0.2 ],
                "A_Nested_Object": {
                    "Another_Simple_Key": "Value",
                    "Nested_Arrays": [
                        [ 10, 0.1 ],
                        [ 0.1, 1 ]
                    ]
                }
            }
        ]
    }
}

Demographics parameters¶

The parameters described in this reference section can be added to the JavaScript Object Notation (JSON) formatted demographics file to determine the demographics of the population within each geographic node in a simulation. For example, the number of individuals and the distribution for age, gender, immunity, risk, and mortality. These parameters work closely with the Population dynamics parameters in the configuration file, which are simulation-wide and generally control whether certain events, such as births or deaths, are enabled in a simulation.

Generally, you will download a demographics file and modify it to meet the needs of your simulation. Demographics files for several locations are available on the Institute for Disease Modeling (IDM) GitHub EMOD-InputData repository or you can use COMputational Platform Service (COMPS) to generate demographics and other input data files for a particular region. By convention, these are named using the name of the region appended with “_demographics.json”, but you may name the file anything you like.

Additionally, you can use more than one demographics file, with one serving as the base layer and the one or more others acting as overlays that override the values in the base layer. This can be helpful if you want to experiment with different values in the overlay without modifying your base file. For more information, see Use demographics overlay files.

At least one demographics file is required for every simulation unless you set the parameter Enable_Demographics_Builtin to 1 (one) in the configuration file. This setting does not represent a real location and is generally only used for testing and validating code pathways rather than actual modeling of disease.

Demographics files are organized into four main sections: Metadata, NodeProperties, Defaults, and Nodes. The following example shows the skeletal format of a demographics file.

{
     "Metadata": {
          "DateCreated": "dateTime",
          "Tool": "scriptUsedToGenerate",
          "Author": "author",
          "IdReference": "Gridded world grump2.5arcmin",
          "NodeCount": 2
     },
     "NodeProperties": [
          {}
     ],
     "Defaults": {
          "NodeAttributes": {},
          "IndividualAttributes": {},
          "IndividualProperties": {},
          "Society": {}
     },
     "Nodes": [{
          "NodeID": 1,
          "NodeAttributes": {},
          "IndividualAttributes": {},
          "IndividualProperties": {},
          "Society": {}
     }, {
          "NodeID": 2,
          "NodeAttributes": {},
          "IndividualAttributes": {},
          "IndividualProperties": {},
          "Society": {}
     }]
}

All parameters except those in the Metadata and NodeProperties sections below can appear in either the Defaults section or the Nodes section of the demographics file. Parameters under Defaults will be applied to all nodes in the simulation. Parameters under Nodes will be applied to specific nodes, overriding the values in Defaults if they appear in both. Each node in the Nodes section is identified using a unique NodeID.

The tables below contain only parameters available when using the tuberculosis simulation type.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

Contents

Metadata
NodeProperties and IndividualProperties
- Transitions
NodeAttributes
IndividualAttributes
- Simple distributions
- Complex distributions

Metadata ¶

Metadata provides information about data provenance. NodeCount and IdReference are the only parameters used by EMOD, but you are encouraged to included information for your own reference. For example, author, date created, tool used, and more are commonly included in the Metadata section. You can include any information you like here provided it is in valid JSON format.

If you generate input data files using COMPS, the following IdReference values are possible and indicate how the NodeID values are generated:

Gridded world grump30arcsec: Nodes are approximately square regions defined by a 30-arc second grid and the NodeID values are generated from the latitude and longitude of the northwest corner.
Gridded world grump2.5arcmin: Nodes are approximately square regions defined by a 2.5-arc minute grid and the NodeID values are generated from the latitude and longitude of the northwest corner.
Gridded world grump1degree: Nodes are approximately square regions defined by a 1-degree grid and the NodeID values are generated from the latitude and longitude of the northwest corner.

The algorithm for encoding latitude and longitude into a NodeID is as follows:

unsigned int xpix = math.floor((lon + 180.0) / resolution)
unsigned int ypix = math.floor((lat + 90.0) / resolution)
unsigned int **NodeID** = (xpix << 16) + ypix + 1

This generates a NodeID that is a 4-byte unsigned integer; the first two bytes represent the longitude of the node and the second two bytes represent the latitude. To reserve 0 to be used as a null value, 1 is added to the NodeID as part of the final calculation.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
IdReference	string	NA	NA	NA	The identifier for a simulation; all input data files used in a simulation must have the same IdReference value. The value must be greater than 0. If the input data files are generated using COMPS, the string that indicates the method used for generating the NodeID, the identifier used for each node in the simulation.	{ "Metadata": { "IdReference": "Gridded world grump30arcsec" } }
NodeCount	integer	1	Depends on available memory	NA	The number of nodes to expect in the input data files.	{ "Metadata": { "NodeCount": 2 } }

NodeProperties and IndividualProperties ¶

Node properties and individual properties are set similarly and share many of the same parameters. Properties can be thought of as tags that are assigned to nodes or individuals and can then be used to either target interventions to nodes or individuals with certain properties (or prevent them from being targeted). For example, you could define individual properties for disease risk and then target an intervention to only those at high risk. Similarly, you could define properties for node accessibility and set lower intervention coverage for nodes that are difficult to access. For more information on setting up properties, see Configure heterogeneity using individual and node properties.

The NodeProperties section is a top-level section at the same level as Defaults and **Nodes that contains parameters that assign properties to nodes in a simulation. The IndividualProperties section is under either Defaults or Nodes and contains parameters that assign properties to individuals in a simulation.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Age_Bin_Edges_In_Years	array	NA	NA	NA	An array of integers that represents the ages, in years, at which to demarcate the age groups for individuals. Used only with the Age_Bin property type. The first number must be 0, the last must be -1, and they must be listed in ascending order. Cannot be used with NodeProperties. EMOD automatically create the individual property Age_Bin with values based on the bin edges using the format Age_Bin_Property_From_X_To_Y. These appear in the property reports and can be used to target campaign interventions using Property_Restrictions_Within_Node. See Target interventions to nodes or individuals for more information.	The following example creates three age groups: 0 to 5, older than 5 to 13, and older than 13 to the maximum age. { "Defaults": { "IndividualProperties": [{ "Property": "Age_Bin", "Age_Bin_Edges_In_Years": [0, 5, 13, -1] }] } }
IndividualProperties	array of objects	NA	NA	NA	An array that contains parameters that add properties to individuals in a simulation. For example, you can define values for accessibility, age, geography, risk, and other properties and assign values to different individuals. alues.	{ "Defaults": { "IndividualProperties": [{ "Property": "InterventionStatus", "Values": ["None", "ARTStaging"], "Initial_Distribution": [1, 0] }, { "Property": "Risk", "Values": ["High", "Medium", "Low"], "Initial_Distribution": [0.2, 0.5, 0.3] }] } }
Initial_Distribution	array of floats	0	1	1	An array of floats that define the proportion of property values to assign to individuals or nodes at the beginning of the simulation and when new individuals are born. Their sum must equal 1 and the number of members in this array must match the number of members in Values. For Age_Bin property types, omit this parameter as the demographics file controls the age distribution.	{ "Nodes": [{ "NodeID": 25, "IndividualProperties": [{ "Initial_Distribution": [0.2, 0.4, 0.4] }] }] } { "Defaults": { "NodeProperties": [{ "Property": "InterventionStatus", "Values": [ "NONE", "RECENT_SPRAY" ], "Initial_Distribution": [ 1.0, 0.0 ] }] } }
NodeProperties	array of objects	NA	NA	NA	An array that contains parameters that add properties to nodes in a simulation. For example, you can define values for intervention status, risk, and other properties and assign values to different nodes.	{ "NodeProperties": [{ "Property": "Risk", "Values": ["HIGH", "MEDIUM", "LOW"], "Initial_Distribution": [0.1, 0.4, 0.5] }] }
Property	enum	NA	NA	NA	The individual or node property type for which you will assign arbitrary values to create groups. You can then move individuals or nodes into or out of different groups or target interventions to particular groups. Possible values are: Age_Bin Assign individuals to age bins. Use with Age_Bin_Edges_In_Years. Cannot be used with NodeProperties. Accessibility Tag individuals or nodes based on their accessibility. Geographic Tag individuals or nodes based on geographic characteristics. HasActiveTB Tag individuals or nodes based on active TB status. Typically used only with HIV ART staging interventions. InterventionStatus Tag individuals or nodes based on intervention status, so that receiving an intervention can affect how other interventions are distributed. Use with Disqualifying_Properties and New_Property_Value in the campaign file. Place Tag individuals or nodes based on place. Risk Tag individuals or nodes based on disease risk. QualityofCare Tag individuals or nodes based on the quality of medical care.	{ "Defaults": { "IndividualProperties": [{ "Property": "Age_Bin", "Age_Bin_Edges_In_Years": [ 0, 6, 10, 20, -1 ] }] } } { "NodeProperties": [{ "Property": "InterventionStatus", "Values": ["NONE", "RECENT_SPRAY"], "Initial_Distribution": [1.0, 0.0] }] }
Transitions	array	NA	NA	NA	An array that contains multiple JSON objects that each define how an individual transitions from one property value to another. See the transitions array table for information about the parameters to include in the Transitions object. For Age_Bin property types, set to an empty array, as individuals will transition to the next age bin based on the passing of time. Cannot be used with NodeProperties.	{ "Defaults": { "IndividualProperties": [{ "Transitions": [{ "From": "High", "To": "Medium", "Type": "At_Age", "Coverage": 1, "Probability_Per_Timestep": 0.3, "Age_In_Years": 5, "Timesteps_Until_Reversion": 0 }, { "From": "Medium", "To": "Low", "Type": "At_Age", "Coverage": 1, "Probability_Per_Timestep": 0.3, "Age_In_Years": 12, "Timesteps_Until_Reversion": 0 }] }] } }
TransmissionMatrix	JSON object	NA	NA	NA	An object that contains Route and Matrix parameters that define how to scale the base infectivity from individuals with one property value to individuals with another. Route must be set to Contact and Matrix contains a WAIFW matrix of the disease transmission multipliers. The rows and columns are in the same order that the property values were defined in Value. The rows represent the infectious individuals (the “whom”); the columns represent the susceptible individuals (the “who”). This implements the HINT feature, which is available only in the generic simulation type. For more information, see Configure heterogeneous disease transmission. Enable_Heterogeneous_Intranode_Transmission in the configuration file must be set to 1 (see Infectivity and transmission parameters). Cannot be used with NodeProperties.	{ "Defaults": { "IndividualProperties": [{ "TransmissionMatrix": { "Route": "Contact", "Matrix": [ [10, 0.1], [0.1, 1] ] } }] } }
Values	array of strings	NA	NA	NA	An array of the user-defined values that can be assigned to individuals or nodes for this property. The order of the values corresponds to the order of the Initial_Distribution array. You can have up to 125 values for the Geographic and InterventionStatus property types and up to 5 values for all other types. For Age_Bin property types, omit this parameter and use Age_Bin_Edges_In_Years instead.	{ "Defaults": { "IndividualProperties": [{ "Values": ["Low", "Medium", "High"] }] } } { "NodeProperties": [ { "Property": "InterventionStatus", "Values": [ "NONE", "RECENT_SPRAY" ], "Initial_Distribution": [ 1.0, 0.0 ] } ] }

Transitions ¶

The Transitions array under IndividualProperties section controls how individuals transition from one property value to another. It cannot be used with NodeProperties. Alternatively, similar transitions can be configured in the campaign file as the result of campaign events.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Age_In_Years	float	0	125	NA	The age at which individuals are eligible to transition. Do not use when Type is set to At_Timestep. Required when Type is set to At_Age.	{ "IndividualProperties": [{ "Transitions": [{ "Type": "At_Age", "Age_In_Years": 10 }] }] }
Age_In_Years_Restriction	JSON object	0	100	3.40E+38	The age when an individual is eligible to transition. Min is optional and Max is required. Do not use this with Type set to At_Age because it will conflict with other age parameters in Age_In_Years. This is required when Type is set to At_Timestep, though it can be empty for no age restriction.	{ "IndividualProperties": [{ "Transitions": [{ "Type": "At_Timestep", "Age_In_Years_Restriction": { "Min": 5, "Max": 40 } }] }] }
Coverage	float	0	1	1	The proportion of the population that EMOD will attempt to transition. The calculation used with Probability_Per_Timeset only uses the proportion of the population specified in Coverage to calculate the probability of an individual transitioning at a given time step. Coverage has no effect when the value is set to 1. Required when Type is set to either At_Age or At_Timestep.	{ "Individual_Properties": [{ "Transitions": [{ "From": "Bad", "To": "Good", "Coverage": 0.5 }] }] }
From	string	NA	NA	NA	The property value that an individual will transition from.	{ "Individual_Properties": [{ "Transitions": [{ "From": "Bad", "To": "Good", "Coverage": 0.5, "Type": "At_Timestep" }] }] }
Probability_Per_Timestep	float	0	1	NA	The daily probability of an individual transitioning given the number of individuals remaining with the same property value. Required when Type is set to At_Age or At_Timestep.	{ "Individual_Properties": { "Transitions": [{ "From": "Low", "To": "High", "Coverage": 0.9, "Type": "At_Timestep", "Probability_Per_Timestep": 0.25 }] } }
Timesteps_Until_Reversion	integer	0	NA	NA	The number of time steps after the start of transitioning when individuals revert back to their original property value. The start of transitioning is specified by the Start parameter in Timestep_Restriction.	{ "Individual_Properties": [{ "Transitions": [{ "Timestep_Restriction": { "Start": 20, "Duration": 60 }, "Timesteps_Until_Reversion": 10 }] }] }
Timestep_Restriction	JSON object	NA	NA	NA	The time step when transitioning starts and stops. Required when Type is set to either At_Age or At_Timestep. Start is required and Duration is optional.	{ "Individual_Properties": [{ "Transitions": [{ "Timestep_Restriction": { "Start": 20, "Duration": 60 } }] }] }
To	string				The property value an individual will transition to.	{ "Individual_Properties": [{ "Transitions": [{ "From": "Bad", "To": "Good", "Coverage": 0.5, "Type": "At_Timestep" }] }] }
Type	enum	NA	NA	NA	The type of condition that starts transitioning individuals. Possible values are At_Age or At_Timestep. The other parameters you must set depend on the condition type set here.	{ "Individual_Properties": [{ "Transitions": [{ "From": "Bad", "To": "Good", "Coverage": 0.5, "Type": "At_Timestep" }] }] }

NodeAttributes ¶

The NodeAttributes section contains parameters that add or modify information regarding the location, migration, habitat, and population of node. Some NodeAttributes depend on values set in the configuration parameters.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Airport	boolean	0	1	0	Indicates whether or not the node has an airport for air migration from (not to) the node. If set to 1, Enable_Air_Migration in the configuration file must be set to 1 or migration will not occur (see Migration parameters). Primarily used to turn off migration in a particular node.	{ "Defaults": { "NodeAttributes": { "Airport": 0 } } }
Altitude	float	-3.40282e+038	3.40282e+038	0	The altitude, in meters, for the node. Required, but only used when Climate_Model is set to CLIMATE_KOPPEN.	{ "Defaults": { "NodeAttributes": { "Altitude": 250 } } }
BirthRate	double	0	1	0.00008715	The birth rate, in births per person per day. In the configuration file, Enable_Birth must be set to 1 and Birth_Rate_Dependence will affect how this rate is used (see Population dynamics parameters).	{ "Nodes": [{ "NodeID": 21, "NodeAttributes": { "BirthRate": 0.0001 } }] }
InitialPopulation	integer	0	2.15E+0	1000	The number of people that will be populated into the node at the beginning of the simulation. You can scale this number using Base_Population_Scale_Factor in the configuration file (see Population dynamics parameters).	{ "Nodes": [{ "NodeID": 25, "NodeAttributes": { "InitialPopulation": 1000 } }] }
Latitude	float	3.40282e+038	-3.40282e+038	-1	Latitude of the node in decimal degrees. This can be used for several things, including determining infectiousness by latitude and defining the size of grid cells.	{ "Nodes": [{ "NodeID": 25, "NodeAttributes": { "Latitude": 12.4, "Longitude": 9.35 } }] }
Longitude	float	-3.40282e+38	3.40282e+38	-1	Longitude of the node in decimal degrees. This can be used for several things, including defining the size of grid cells.	{ "Nodes": [{ "NodeID": 254, "NodeAttributes": { "Latitude": 25.4, "Longitude": 9.1 } }] }
NodeAttributes	JSON object	NA	NA	NA	The structure that contains parameters that add or modify information regarding the location, migration, habitat, and population of a simulation. Some NodeAttributes depend on values set in the configuration parameters.	{ "Nodes": [ { "NodeID": 1487548419, "NodeAttributes": { "Latitude": 12.4208, "Longitude": 9.15417 } } ] }
Region	boolean	0	1	0	Indicates whether or not the node has a road network for regional migration from (not to) the node. If set to 1, Enable_Regional_Migration in the configuration file must be set to 1 or migration will not occur (see Migration parameters). Primarily used to turn off migration in particular nodes.	{ "Nodes": [{ "NodeID": 12, "NodeAttributes": { "Region": 1 } }] }
Seaport	boolean	0	1	0	Indicates whether or not the node is connected by sea migration from (not to) the node. If set to 1, Enable_Sea_Migration in the configuration file must be set to 1 or migration will not occur (see Migration parameters). Primarily used to turn off migration in particular nodes.	{ "Nodes": [{ "NodeID": 43, "NodeAttributes": { "Seaport": 1 } }] }
Urban	boolean	0	1	0	Indicates whether urban settings are enabled. Used only if Enable_Demographics_Other is set to 1 and Birth_Rate_Dependence is set to INDIVIDUAL_PREGNANCIES_BY_URBAN_AND_AGE and required if Enable_Demographics_Other is set to 1 in the configuration file (see Population dynamics parameters).	{ "Defaults": { "NodeAttributes": { "Urban": 1 } } }
Zoonosis	float	0	1	0	The daily rate of zoonotic infection per individual. In the configuration file, Animal_Reservoir_Type must be set to ZOONOSIS_FROM_DEMOGRAPHICS to use this value (see General disease parameters).	{ "Defaults": { "NodeAttributes": { "Zoonosis": 0 } } }

IndividualAttributes ¶

The IndividualAttributes section contains parameters that initialize the distribution of attributes across individuals, such as the age or immunity. An initial value for an individual is a randomly selected value from a given distribution. These distributions can be configured using a simple flag system of three parameters or a complex system of many more parameters. The following table contains the parameters that can be used with either distribution system.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
IndividualAttributes	JSON object	NA	NA	NA	The structure that contains parameters that add or modify the distribution of attributes across individuals in a simulation. For example, the age or immunity distribution. An initial value for an individual is a randomly selected value from a distribution. For example, if you use a uniform distribution to initialize age, the initial ages of individuals in the simulation will be evenly distributed between some minimum and maximum value.	{ "Defaults": { "IndividualAttributes": { "AgeDistributionFlag": 0, "AgeDistribution1": 25550, "AgeDistribution2": 0 } } }
PercentageChildren	float	0	1	NA	The percentage of individuals in the node that are children. Set Minimum_Adult_Age_Years to determine the age at which individuals transition to adults.	{ "Nodes": { "NodeID": 45, "IndividualAttributes": { "PercentageChildren": 0.7 } } }

Simple distributions ¶

Simple distributions are defined by three parameters where one is a flag for the distribution type and the other two are used to further define the distribution. For example, if you set the age flag to a uniform distribution, the initial ages of individuals in the simulation will be evenly distributed between some minimum and maximum value as defined by the other two parameters.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

AgeDistribution1

float

-3.40282e+038

3.40282e+038

0.000118

The first value, in days, in the age distribution, the meaning of which depends upon the value set in AgeDistributionFlag. The table below shows the flag value and corresponding distribution value.

AgeDistributionFlag	AgeDistribution1
0	Age value to assign.
1	Minimum age for a uniform distribution.
2	Mean age for a Gaussian distribution.
3	Mean age for an exponential distribution.
4	Mean age for a Poisson distribution.
5	Mu for a log normal distribution.
6	Proportion of time to return the value specified by AgeDistribution2 for a bimodal distribution. Must be between 0 and 1.
7	Scale parameter for a Weibull distribution.

Age_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_SIMPLE (see Population dynamics parameters).

{
    "IndividualAttributes": {
        "AgeDistributionFlag": 0,
        "AgeDistribution1": 25550,
        "AgeDistribution2": 0
    }
}

AgeDistribution2

float

-3.40282e+038

3.40282e+038

0

The second value, in days, in the age distribution, the meaning of which depends upon the value set in AgeDistributionFlag. The table below shows the flag value and corresponding distribution value.

AgeDistributionFlag	AgeDistribution2
0	NA, set to 0.
1	Maximum age for a uniform distribution.
2	Standard deviation in age for a Gaussian distribution.
3	NA, set to 0.
4	NA, set to 0.
5	Sigma for a log normal distribution.
6	A positive value to be returned for a bimodal distribution. If the value is not positive, a value of 1 is returned.
7	Shape parameter for a Weibull distribution.

Age_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_SIMPLE (see Population dynamics parameters).

{
    "IndividualAttributes": {
        "AgeDistributionFlag": 0,
        "AgeDistribution1": 25550,
        "AgeDistribution2": 0
    }
}

AgeDistributionFlag

integer

0

7

3

The type of distribution to use for age. Possible values are:

0 (Constant, everyone in the population is the same age.)
1 (Uniform, ages are randomly drawn between a minimum and maximum value.)
2 (Gaussian)
3 (Exponential)
4 (Poisson)
5 (Log normal)
6 (Bimodal)
7 (Weibull)

Age_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_SIMPLE (see Population dynamics parameters).

{
    "IndividualAttributes": {
        "AgeDistributionFlag": 0,
        "AgeDistribution1": 25550,
        "AgeDistribution2": 0
    }
}

ImmunityDistribution1

float

-3.40282e+038

3.40282e+038

0

The first value in the immunity distribution, the meaning of which depends upon the value set in ImmunityDistributionFlag. Enable_Immunity in the configuration file must be set to 1. The table below shows the flag value and corresponding distribution value.

ImmunityDistributionFlag	ImmunityDistribution1
0	Immunity value to assign.
1	Minimum immunity for a uniform distribution.
2	Mean immunity for a Gaussian distribution.
3	Mean immunity for an exponential distribution.
4	Mean immunity for a Poisson distribution.
5	Mu for a log normal distribution.
6	Proportion of time to return the value specified by ImmunityyDistribution2 for a bimodal distribution. Must be between 0 and 1.
7	Scale parameter for a Weibull distribution.

In the configuration file, Enable_Demographics_Other must be set to true (1) and Immunity_Initialization_Distribution_Type must be set to DISTRIBUTION_SIMPLE (see Immunity parameters).

{
    "IndividualAttributes": { 
        "ImmunityDistributionFlag": 0, 
        "ImmunityDistribution1": 1, 
        "ImmunityDistribution2": 0 
    }
}

ImmunityDistribution2

float

-3.40282e+038

3.40282e+038

0

The second value in the distribution, the meaning of which depends upon the value set in ImmunityDistributionFlag. Enable_Immunity in the configuration file must be set to 1. The table below shows the flag value and corresponding distribution value.

ImmunityDistributionFlag	ImmunityDistribution2
0	NA, set to 0.
1	Maximum immunity for a uniform distribution.
2	Standard deviation in immunity for a Gaussian distribution.
3	NA, set to 0.
4	NA, set to 0.
5	Sigma for a log normal distribution.
6	A positive value to be returned for a bimodal distribution. If the value is not positive, a value of 1 is returned.
7	Shape parameter for a Weibull distribution.

In the configuration file, Enable_Demographics_Other must be set to true (1) and Immunity_Initialization_Distribution_Type must be set to DISTRIBUTION_SIMPLE (see Immunity parameters).

{
    "IndividualAttributes": { 
        "ImmunityDistributionFlag": 0, 
        "ImmunityDistribution1": 1, 
        "ImmunityDistribution2": 0 
    }
}

ImmunityDistributionFlag

integer

0

7

0

The type of distribution to use for immunity. Enable_Immunity in the configuration file must be set to 1. Possible values are:

0 (Constant, everyone in the population has the same immunity.)
1 (Uniform, immunity is randomly drawn between a minimum and maximum value.)
2 (Gaussian)
3 (Exponential)
4 (Poisson)
5 (Log normal)
6 (Bimodal)
7 (Weibull)

In the configuration file, Enable_Demographics_Other must be set to true (1) and Immunity_Initialization_Distribution_Type must be set to DISTRIBUTION_SIMPLE (see Immunity parameters).

{
    "IndividualAttributes": { 
        "ImmunityDistributionFlag": 0, 
        "ImmunityDistribution1": 1, 
        "ImmunityDistribution2": 0 
    }
}

MigrationHeterogeneityDistribution1

float

-3.40282e+38

3.40282e+38

1

The first value in the migration heterogeneity distribution, the meaning of which depends upon the value set in MigrationHeterogeneityFlag. The table below shows the flag value and corresponding distribution value.

MigrationHeterogeneityDistributionFlag	MigrationHeterogeneityDistribution1
0	Migration heterogeneity value to assign.
1	Minimum migration heterogeneity for a uniform distribution.
2	Mean migration heterogeneity for a Gaussian distribution.
3	Mean migration heterogeneity for an exponential distribution.
4	Mean migration heterogeneity for a Poisson distribution.
5	Mu for a log normal distribution.
6	Proportion of time to return the value specified by MigrationHeterogeneityDistribution2 for a bimodal distribution. Must be between 0 and 1.
7	Scale parameter for a Weibull distribution.

Enable_Migration_Heterogeneity in the configuration file must be set to 1 (see Migration parameters).

{
    "IndividualAttributes": {
        "MigrationHeterogeneityDistributionFlag": 0, 
        "MigrationHeterogeneityDistribution1": 1, 
        "MigrationHeterogeneityDistribution2": 0
    }
}

MigrationHeterogeneityDistribution2

float

-3.40282e+038

3.40282e+038

0

The second value in the distribution, the meaning of which depends upon the value set in MigrationHeterogeneityDistributionFlag. The table below shows the flag value and corresponding distribution value.

MigrationHeterogeneityDistributionFlag	MigrationHeterogeneityDistribution2
0	NA, set to 0.
1	Maximum migration heterogeneity for a uniform distribution.
2	Standard deviation in migration heterogeneity for a Gaussian distribution.
3	NA, set to 0.
4	NA, set to 0.
5	Sigma for a log normal distribution.
6	A positive value to be returned for a bimodal distribution. If the value is not positive, a value of 1 is returned.
7	Shape parameter for a Weibull distribution.

Enable_Migration_Heterogeneity in the configuration file must be set to 1 (see Migration parameters).

{
    "IndividualAttributes": {
        "MigrationHeterogeneityDistributionFlag": 0, 
        "MigrationHeterogeneityDistribution1": 1, 
        "MigrationHeterogeneityDistribution2": 0
    }
}

MigrationHeterogeneityDistributionFlag

integer

0

7

0

The type of distribution to use for migration heterogeneity. Possible values are:

0 (Constant, everyone in the population has the same migration value.)
1 (Uniform, migration values are randomly drawn between a minimum and maximum value.)
2 (Gaussian)
3 (Exponential)
4 (Poisson)
5 (Log normal)
6 (Bimodal)
7 (Weibull)

Enable_Migration_Heterogeneity in the configuration file must be set to 1 (see Migration parameters).

{
    "IndividualAttributes": {
        "MigrationHeterogeneityDistributionFlag": 0, 
        "MigrationHeterogeneityDistribution1": 1, 
        "MigrationHeterogeneityDistribution2": 0
    }
}

PrevalenceDistribution1

float

-3.40282e+038

3.40282e+038

1

The first value in the prevalence distribution, the meaning of which depends upon the value set in PrevalenceDistributionFlag. The table below shows the flag value and corresponding distribution value.

PrevalenceDistributionFlag	PrevalenceDistribution1
0	Prevalence value to assign.
1	Minimum prevalence for a uniform distribution.
2	Mean prevalence for a Gaussian distribution.
3	Mean prevalence for an exponential distribution.
4	Mean prevalence for a Poisson distribution.
5	Mu for a log normal distribution.
6	Proportion of time to return the value specified by PrevalenceDistribution2 for a bimodal distribution. Must be between 0 and 1.
7	Scale parameter for a Weibull distribution.

Enable_Demographics_Other must be set to 1 in the configuration file (see Population dynamics parameters).

{
    "IndividualAttributes": { 
        "PrevalenceDistributionFlag": 0, 
        "PrevalenceDistribution1": 0.0, 
        "PrevalenceDistribution2": 0.0
    }
} 

PrevalenceDistribution2

float

-3.40282e+038

3.40282e+038

0

The second value in the distribution, the meaning of which depends upon the value set in PrevalenceDistributionFlag. The table below shows the flag value and corresponding distribution value.

PrevalenceDistributionFlag	PrevalenceDistribution2
0	NA, set to 0.
1	Maximum prevalence for a uniform distribution.
2	Standard deviation in prevalence for a Gaussian distribution.
3	NA, set to 0.
4	NA, set to 0.
5	Sigma for a log normal distribution.
6	A positive value to be returned for a bimodal distribution. If the value is not positive, a value of 1 is returned.
7	Shape parameter for a Weibull distribution.

Enable_Demographics_Other must be set to 1 in the configuration file (see Population dynamics parameters).

{
    "IndividualAttributes": { 
        "PrevalenceDistributionFlag": 0, 
        "PrevalenceDistribution1": 0.0, 
        "PrevalenceDistribution2": 0.0
    }
} 

PrevalenceDistributionFlag

integer

0

7

0

The type of distribution to use for prevalence. Possible values are:

0 (Constant, everyone in the population has the same prevalence.)
1 (Uniform, prevalence is randomly drawn between a minimum and maximum value.)
2 (Gaussian)
3 (Exponential)
4 (Poisson)
5 (Log normal)
6 (Bimodal)
7 (Weibull)

Enable_Demographics_Other must be set to 1 in the configuration file (see Population dynamics parameters).

{
    "IndividualAttributes": { 
        "PrevalenceDistributionFlag": 0, 
        "PrevalenceDistribution1": 0.0, 
        "PrevalenceDistribution2": 0.0
    }
} 

RiskDistribution1

float

-3.40282e+038

3.40282e+038

0

The first value in the risk distribution, the meaning of which depends upon the value set in RiskDistributionFlag. The table below shows the flag value and corresponding distribution value.

RiskDistributionFlag	RiskDistribution1
0	Risk value to assign.
1	Minimum risk for a uniform distribution.
2	Mean risk for a Gaussian distribution.
3	Mean risk for an exponential distribution.
4	Mean risk for a Poisson distribution.
5	Mu for a log normal distribution.
6	Proportion of time to return the value specified by RiskDistribution2 for a bimodal distribution. Must be between 0 and 1.
7	Scale parameter for a Weibull distribution.

Enable_Demographics_Other must be set to 1 in the configuration file (see Population dynamics parameters).

{
    "IndividualAttributes": { 
        "RiskDistributionFlag": 0, 
        "RiskDistribution1": 1, 
        "RiskDistribution2": 0
    }
}

RiskDistribution2

float

-3.40282e+038

3.40282e+038

0

The second value in the distribution, the meaning of which depends upon the value set in RiskDistributionFlag. The table below shows the flag value and corresponding distribution value.

RiskDistributionFlag	RiskDistribution2
0	NA, set to 0.
1	Maximum risk for a uniform distribution.
2	Standard deviation in risk for a Gaussian distribution.
3	NA, set to 0.
4	NA, set to 0.
5	Sigma for a log normal distribution.
6	A positive value to be returned for a bimodal distribution. If the value is not positive, a value of 1 is returned.
7	Shape parameter for a Weibull distribution.

Enable_Demographics_Other must be set to 1 in the configuration file (see Population dynamics parameters).

{
    "IndividualAttributes": { 
        "RiskDistributionFlag": 0, 
        "RiskDistribution1": 1, 
        "RiskDistribution2": 0
    }
}

RiskDistributionFlag

integer

0

7

0

The type of distribution to use for risk. Possible values are:

0 (Constant, everyone in the population has the same risk.)
1 (Uniform, risk is randomly drawn between a minimum and maximum value.)
2 (Gaussian)
3 (Exponential)
4 (Poisson)
5 (Log normal)
6 (Bimodal)
7 (Weibull)

Enable_Demographics_Other must be set to 1 (see Population dynamics parameters).

{
    "IndividualAttributes": { 
        "RiskDistributionFlag": 0, 
        "RiskDistribution1": 1, 
        "RiskDistribution2": 0
    }
}

Complex distributions ¶

Complex distributions are more effort to configure, but are useful for representing real-world data where the distribution does not fit a standard. Individual attribute values are drawn from a piecewise linear distribution. The distribution is configured using arrays of axes (such as gender or age) and values at points along each of these axes. This allows you to have different distributions for different groups in the population.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
AgeDistribution	JSON object	NA	NA	NA	The structure defining a complex age distribution. Age_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX.	The following example shows at age distribution in which 25% of individuals are under age 5, 50% are between 5 and 20, and 25% are between 20 and 35. { "IndividualAttributes": { "AgeDistribution": { "ResultUnits": "years", "ResultScaleFactor": 365, "ResultValues": [0, 0.25, 0.75, 1], "DistributionValues": [0, 5, 20, 35] } } }
AxisNames	array of strings	NA	NA	NA	An array of the names used for each axis of a complex distribution. The list below shows the axis names to use (in the order given) for each of the distribution types: MortalityDistribution [“gender”, “age”] Death_Rate_Dependence in the configuration file must be set to NONDISEASE_MORTALITTY_BY_AGE_AND_GENDER (see Mortality and survival parameters). MortalityDistributionMale [“age”, “year”] Death_Rate_Dependence must be set to NONDISEASE_MORTALITY_BY_YEAR_AND_AGE_FOR_EACH_GENDER (see Mortality and survival parameters). MortalityDistributionFemale [“age”, “year”] Death_Rate_Dependence must be set to NONDISEASE_MORTALITY_BY_YEAR_AND_AGE_FOR_EACH_GENDER (see Mortality and survival parameters). FertilityDistribution Two options are available: [“urban”, “age”] Birth_Rate_Dependence in the configuratIon file must be set to INDIVIDUAL_PREGNANCIES_BY_URBAN_AND_AGE (see Population dynamics parameters). [“age”, “year”] Birth_Rate_Dependence must be set to INDIVIDUAL_PREGNANCIES_BY_AGE_AND_YEAR (see Population dynamics parameters). ImmunityDistribution [“age”] AgeDistribution No axes.	{ "IndividualAttributes": { "MortalityDistribution": { "AxisNames": ["gender", "age"], "AxisUnits": ["male=0,female=1", "years"], "AxisScaleFactors": [1, 365], "NumPopulationGroups": [2, 1], "PopulationGroups": [ [0, 1], [0] ], "ResultUnits": "annual deaths per 1000 individuals", "ResultScaleFactor": 2.739726027397e-06, "ResultValues": [ [0], [0] ] } } }
AxisScaleFactors	array of floats	3.40282e+038	-3.40282e+038	1	A list of the scale factors used to convert axis units to data measurements in a complex distribution. For example, 365 to convert daily mortality to annual mortality. The array must contain one factor for each axis.	{ "IndividualAttributes": { "MortalityDistribution": { "AxisNames": ["gender", "age"], "AxisUnits": ["male=0,female=1", "years"], "AxisScaleFactors": [1, 365], "NumPopulationGroups": [2, 1], "PopulationGroups": [ [0, 1], [0] ], "ResultUnits": "annual deaths per 1000 individuals", "ResultScaleFactor": 2.739726027397e-06, "ResultValues": [ [0], [0] ] } } }
AxisUnits	array of floats	0	3.40E+3	1	An array of the scale factors to use to convert the units used for the axes into days. EMOD does not use this value; it is only informational.	{ "IndividualAttributes": { "MortalityDistribution": { "AxisNames": ["gender", "age"], "AxisUnits": ["male=0,female=1", "years"], "AxisScaleFactors": [1, 365] } } }
DistributionValues	array of floats	0	1	1	An array of values between 0 and 1 listed in ascending order that defines a complex age distribution. Each value represents the proportion of the population below that age and the difference between two successive values is the proportion of the population in the age bin defined in ResultValues. Age_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX (see Population dynamics parameters).	The following example shows at age distribution in which 25% of individuals are under age 5, 50% are between 5 and 20, and 25% are between 20 and 35. { "IndividualAttributes": { "AgeDistribution": { "ResultUnits": "years", "ResultScaleFactor": 365, "AxisScaleFactors": 1, "DistributionValues": [0, 0.25, 0.75, 1], "ResultValues": [0, 5, 20, 35] } } }
FertilityDistribution	JSON object	NA	NA	NA	The distribution of the fertility rate in the population. Enable_Birth in the configuration file must be set to 1 (see Population dynamics parameters).	{ "IndividualAttributes": { "FertilityDistribution": { "NumDistributionAxes": 2, "AxisNames": ["urban", "XXX"], "AxisUnits": ["rural=0, urban=1", "years"], "AxisScaleFactors": [1, 365], "NumPopulationGroups": [2, 9], "PopulationGroups": [ [0, 1], [0, 15, 20, 25, 30, 35, 40, 45, 49] ], "ResultUnits": "annual births per 1000 individuals", "ResultScaleFactor": 0.000002739726027397, "ResultValues": [ [0, 28.4, 190.3, 222.4, 155.4, 68, 21.9, 3.6, 0], [0, 28.4, 190.3, 222.4, 155.4, 68, 21.9, 3.6, 0] ] } } }
ImmunityDistribution	JSON object	NA	NA	NA	The structure defining a complex immunity distribution. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX (see Immunity parameters).	{ "IndividualAttributes": { "ImmunityDistribution": { "AxisNames": ["age"], "AxisUnits": ["years"], "AxisScaleFactors": [365], "NumPopulationGroups": [1], "PopulationGroups": [ [0] ], "ResultScaleFactor": 3.6952, "ResultValues": [ [0] ] } } }
MortalityDistribution	JSON object	NA	NA	NA	The distribution of non-disease mortality for a population. Death_Rate_Dependence in the configuration file must be set to NONDISEASE_MORTALITY_BY_AGE_AND_GENDER or NONDISEASE_MORTALITY_BY_YEAR_AND_AGE_FOR_EACH_GENDER (see Mortality and survival parameters). Warning Mortality is sampled every 30 days. To correctly attribute neonatal deaths to days 0-30, you must indicate that the threshold for the first age group in PopulationGroups is less than 30 days.	{ "IndividualAttributes": { "MortalityDistribution": { "AxisNames": [ "gender", "age" ], "AxisScaleFactors": [ 1, 1 ], "NumDistributionAxes": 2, "NumPopulationGroups": [ 2, 4 ], "PopulationGroups": [ [ 0, 1 ], [ 0.0, 29.99, 365, 1826 ] ], "ResultScaleFactor": 1, "ResultValues": [ [ 0.0016, 0.000107, 6.3e-05, 100.0 ], [ 0.0016, 0.000107, 6.3e-05, 100.0 ] ] } } }
NumDistributionAxes	integer	1	NA	NA	The number of axes to use for a complex distribution. EMOD does not use this value; it is only informational.	{ "IndividualAttributes": { "MortalityDistribution": { "NumDistributionAxes": 2, "AxisNames": ["gender", "age"], "AxisScaleFactors": [1, 365] } } }
NumPopulationGroups	array of integers	NA	NA	NA	An array of population groupings for each independent variable for a complex distribution. This variable defines the number of columns for each row in the population group table. The number of values in the array is often two, representing the values for gender and number of age bins. EMOD does not use this value; it is only informational.	{ "IndividualAttributes": { "MortalityDistribution": { "AxisNames": ["gender", "age"], "AxisUnits": ["male=0,female=1", "years"], "AxisScaleFactors": [1, 365], "NumPopulationGroups": [2, 1], "PopulationGroups": [ [0, 1], [0] ], "ResultUnits": "annual deaths per 1000 individuals", "ResultScaleFactor": 2.739726027397e-06, "ResultValues": [ [0], [0] ] } } }
PopulationGroups	matrix of integers	NA	NA	NA	An array in which each row represents one of the distribution axes and contains the values that the independent variable can take. The values must be listed in ascending order and each defines the left edge of the bin. Warning Mortality is sampled every 30 days. To correctly attribute neonatal deaths to days 0-30, you must indicate that the threshold for the first age group in PopulationGroups is less than 30 days.	The following example configures relatively high infant mortality and lower mortality at ages 10 and 40, with everyone dead by age 120. { "IndividualAttributes": { "MortalityDistribution": { "AxisNames": ["gender", "age"], "AxisUnits": ["male=0,female=1", "years"], "AxisScaleFactors": [1, 365], "NumPopulationGroups": [2, 1], "PopulationGroups": [ [0, 1], [0, 10, 40, 120] ], "ResultUnits": "annual deaths per 1000 individuals", "ResultScaleFactor": 2.739726027397e-06, "ResultValues": [ [51.6, 3.7, 5.3, 1000], [60.1, 4.1, 4.8, 1000] ] } } }
ResultScaleFactor	float	-3.40282e+038	3.40282e+038	1	The scale factor used to convert ResultUnits to number of births, deaths, or another variable per individual per day.	{ "IndividualAttributes": { "AgeDistribution": { "AxisScaleFactors": 1, "DistributionValues": [ 0.99, 1.0 ], "ResultScaleFactor": 365, "ResultUnits": "years", "ResultValues": [ 0.0027, 0.0027 ] } } }
ResultUnits	string	NA	NA	NA	A string that indicates the units used for the Results parameters of a complex distribution. EMOD does not use this value; it is only informational.	{ "IndividualAttributes": { "MortalityDistribution": { "NumPopulationGroups": [2, 1], "PopulationGroups": [ [0, 1], [0] ], "ResultUnits": "annual deaths per 1000 individuals", "ResultScaleFactor": 2.739726027397e-06, "ResultValues": [ [0], [0] ] } } }
ResultValues	array of floats	NA	NA	NA	An array that contains floats in the units set by ResultUnits. For age distributions, it lists in ascending order the ages at which to bin the population. The corresponding values in DistributionValues represent the proportion of the population that is below that age. If the first member of the array is non-zero, the first bin is defined as those with that exact value (EMOD does not assume the bins start at zero). For all other distributions, an array in which each row represents the values for a combination of axes. For example, a mortality distribution that includes both gender and age axes will have a row for males and a row for females that each contain the mortality rate at various ages set in PopulationGroups.	The following example shows an age distribution in which 10% of individuals are newborn, 25% are under age 5, 50% are between 5 and 20, and 25% are between 20 and 35. { "IndividualAttributes": { "AgeDistribution": { "DistributionValues": [0.1, 0.25, 0.75, 1], "ResultValues": [0, 5, 20, 35] } } } The following example configures relatively high infant mortality and lower mortality at ages 10 and 40, with everyone dead by age 120. { "IndividualAttributes": { "MortalityDistribution": { "AxisNames": ["gender", "age"], "AxisUnits": ["male=0,female=1", "years"], "AxisScaleFactors": [1, 365], "NumPopulationGroups": [2, 1], "PopulationGroups": [ [0, 1], [0, 10, 40, 120] ], "ResultUnits": "annual deaths per 1000 individuals", "ResultScaleFactor": 2.739726027397e-06, "ResultValues": [ [51.6, 3.7, 5.3, 1000], [60.1, 4.1, 4.8, 1000] ] } } }

Configuration parameters¶

The parameters described in this reference section can be added to the JavaScript Object Notation (JSON) formatted configuration file to determine the core behavior of a simulation including the computing environment, functionality to enable, additional files to use, and characteristics of the disease being modeled. This file contains mostly a flat list of JSON key:value pairs. For more information on how to use these files, see Create a configuration file.

The tables below contain only parameters available when using the tuberculosis simulation type. Some parameters may appear in multiple categories.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

Contents

Disease progression
Drugs and treatments
Enable or disable features
General disease
Geography and environment
Immunity
Incubation
Infectivity and transmission
Input data files
Migration
Mortality and survival
Output settings
Population dynamics
Sampling
Scalars and multipliers
Simulation setup
Symptoms and diagnosis

Disease progression ¶

The following parameters determine the characteristics of tuberculosis progression, such as incubation, latency, activation, and whether progression is fast or slow.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
TB_Active_Cure_Rate	float	0	1	0	The mean rate at which active symptomatic individuals progress to a cured (uninfected) state. In the absence of treatment, this rate is added to TB_Inactivation_Rate and TB_Active_Mortality_Rate to determine the total rate of the active smear-positive state, then the ratio of TB_Active_Cure_Rate to total rate determines the probability that an actively infected individual will go to a cured state after a mean duration of 1 / total rate.	{ "TB_Active_Cure_Rate": 1e-09 }
TB_Active_Period_Distribution	enum	NA	NA	NOT_INITIALIZED	Distribution for duration of active disease. For all possible values, the mean is defined by total rate, which is the sum of TB_Inactivation_Rate and TB_Active_Mortality_Rate corrected for smear status. Possible values are: NOT_INITIALIZED No distribution set. FIXED_DURATION A constant duration incubation period. UNIFORM_DURATION A uniform random draw for the duration of the incubation period. GAUSSIAN_DURATION Duration of the active period is defined by the mean and standard deviation (defined by TB_Active_Period_Std_Dev) of the Gaussian distribution. Negative values are truncated at zero. EXPONENTIAL_DURATION The active period is the mean of the exponential random draw. POISSON_DURATION The active period is the mean of the random Poisson draw. LOG_NORMAL_DURATION The active period is a log normal distribution defined by a mean and log width. BIMODAL_DURATION The distribution is bimodal, the duration is a fraction of the active period for a specified period of time and equal to the active period otherwise. PIECEWISE_CONSTANT The distribution is specified with a list of years and a matching list of values. The duration at a given year is that specified for the nearest previous year. PIECEWISE_LINEAR The distribution is specified with a list of years and matching list of values. The duration at a given year is a linear interpolation of the specified values. WEIBULL_DURATION The duration is a Weibull distribution with a given scale and shape. DUAL_TIMESCALE_DURATION The duration is two exponential distributions with given means.	{ "TB_Active_Period_Distribution": "EXPONENTIAL_DURATION" }
TB_Active_Period_Std_Dev	float	0	3.40E+38	1	The standard deviation for the distribution of the duration of active TB. Used when TB_Active_Period_Distribution is set to GAUSSIAN_DURATION.	{ "TB_Active_Period_Std_Dev": 200 }
TB_Active_Presymptomatic_Infectivity_Multiplier	float	0	1	0.0274	The scale factor for Base_Infectivity to give the infectiousness of active presymptomatic individuals.	{ "TB_Active_Presymptomatic_Infectivity_Multiplier": 0.19999999999999998 }
TB_Extrapulmonary_Fraction_Adult	float	0	1	0	The fraction of active cases in adults (age 15 or over) that are extrapulmonary. The sum of this and TB_Smear_Positive_Fraction_Adult must be less than or equal to 1.	{ "TB_Extrapulmonary_Fraction_Adult": 0.33 }
TB_Extrapulmonary_Fraction_Child	float	0	1	0	The fraction of active cases in children (under age 15) that are extrapulmonary. The sum of this and TB_Smear_Positive_Fraction_Child must be less than or equal to 1.	{ "TB_Extrapulmonary_Fraction_Child": 0.33 }
TB_Extrapulmonary_Mortality_Multiplier	float	0	1	0.4	The scale factor for TB_Active_Mortality_Rate to give the mortality rate of active extrapulmonary individuals. In the absence of treatment, the product is added to TB_Active_Cure_Rate and TB_Inactivation_Rate to determine the total rate of the active extrapulmonary state, then the ratio of the aforementioned product to total rate determines the probability that an active extrapulmonary individual will die from TB disease after mean duration of 1 / total rate.	{ "TB_Extrapulmonary_Mortality_Multiplier": 1 }
TB_Fast_Progressor_Fraction_Adult	float	0	1	1	The fraction of adults (age 15 or over) that are fast progressors.	{ "TB_Fast_Progressor_Fraction_Adult": 0.5 }
TB_Fast_Progressor_Fraction_Child	float	0	1	1	The fraction of children (under age 15) that are fast progressors.	{ "TB_Fast_Progressor_Fraction_Child": 0.5 }
TB_Fast_Progressor_Rate	float	0	1	4.11E-05	The mean rate at which individuals who are fast progressors progress from a latently infected state to an active presymptomatic state. This rate is added to the TB_Latent_Cure_Rate to determine the total rate of the latently infected state, then the ratio of TB_Fast_Progressor_Rate to the total rate is calculated. This ratio determines the probability that a latently infected fast progressor will go to an active presymptomatic state after a mean duration of 1 / total rate.	{ "TB_Fast_Progressor_Rate": 0.02 }
TB_Inactivation_Rate	float	0	1	0.00041096	The mean rate at which active symptomatic (smear-positive, smear-negative, and extrapulmonary) individuals progress to an inactivated (latently infected) state. In the absence of treatment, this rate is added to TB_Active_Cure_Rate and TB_Active_Mortality_Rate to determine the total rate of the active symptomatic state, then the ratio of TB_Active_Cure_Rate to total rate determines the probability that an active syptomatic individual will go to a latently infected state after a mean duration of 1 / total rate.	{ "TB_Inactivation_Rate": 0.05 }
TB_Latent_Cure_Rate	float	0	1	0.0005479	The mean rate at which individuals progress from a latently infected state to a cured (uninfected) state. This rate is added to either the TB_Fast_Progressor_Rate or the TB_Slow_Progressor_Rate to determine the total rate of the latently infected state (depending on whether the individual is a fast progressor or slow progressor, respectively), then the ratio of TB_Latent_Cure_Rate to total rate is calculated. This ratio determines the probability that a latently infected individual will go to a cured state.	{ "TB_Latent_Cure_Rate": 0.1 }
TB_Presymptomatic_Cure_Rate	float	0	1	0.0274	The mean rate at which active presymptomatic individuals progress to a cured (uninfected) state. This rate is added to TB_Presymptomatic_Rate to determine the total rate of the active presymptomatic state, then the ratio of TB_Presymptomatic_Cure_Rate to total rate is calculated. This ratio determines the probability that an active presymptomatic individual will go to a cured state after a mean duration of 1 / total rate.	{ "TB_Presymptomatic_Cure_Rate": 0.05 }
TB_Presymptomatic_Rate	float	0	1	0.0274	The mean rate at which active presymptomatic individuals progress to an active symptomatic state. This rate is added to TB_Presymptomatic_Cure_Rate to determine the total rate of the active presymptomatic state, then the ratio of TB_Presymptomatic_Rate to total rate is calculated. This ratio determines the probability that an active presymptomatic individual will go to active symptomatic disease after a mean duration of 1 / total rate.	{ "TB_Presymptomatic_Rate": 0.0333 }
TB_Relapsed_to_Active_Rate	float	0	1	4.11E-05	The mean rate at which relapsed individuals progress to an active symptomatic state.	{ "TB_Relapsed_to_Active_Rate": 0.01 }
TB_Slow_Progressor_Rate	float	0	1	2.05E-06	The mean rate at which individuals who are slow progressors progress from a latently infected state to an active presymptomatic state. This rate is added to the TB_Latent_Cure_Rate to determine the total rate of the latently infected state, then the ratio of TB_Slow_Progressor_Rate to the total rate is calculated. This ratio determines the probability that a latently infected fast progressor will go to an active presymptomatic state after a mean duration of 1 / total rate.	{ "TB_Slow_Progressor_Rate": 0.02 }
TB_Smear_Positive_Fraction_Adult	float	0	1	1	The fraction of active cases in adults (age 15 and over) that are smear-positive. The sum of TB_Smear_Positive_Fraction_Adult and TB_Extrapulmonary_Fraction_Adult must be less than or equal to 1. This sum plus the fraction of smear-negative adults is equal to 1.	{ "TB_Smear_Positive_Fraction_Adult": 1 }
TB_Smear_Positive_Fraction_Child	float	0	1	1	The fraction of active cases in children (under age 15) that are smear-positive. The sum of TB_Smear_Positive_Fraction_Child and TB_Extrapulmonary_Fraction_Child must be less than or equal to 1. This sum plus the fraction of smear-negative children is equal to 1.	{ "TB_Smear_Positive_Fraction_Child": 1 }

Drugs and treatments ¶

The following parameters determine the efficacy of drugs and other treatments.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
TB_Drug_Clearance_Rate	float	0	1	1	The daily rate at which treatment with an anti-TB drug causes disease clearance. This parameter, TB_Drug_Inactivation_Rate, TB_Drug_Mortality_Rate, and TB_Drug_Relapse_Rate specify the daily probability of a change in the TB disease state during the course of treatment. The sum of these rates and the total duration of treatment specify the probability that any event occurs. If no event occurs, the individual fails drug treatment.	{ "TB_Drug_Params": { "BurninDrug": { "TB_Drug_Clearance_Rate": 0.0111, "TB_Drug_Inactivation_Rate": 0, "TB_Drug_Mortality_Rate": 0.00162, "TB_Drug_Relapse_Rate": 0.00223, "TB_Drug_Resistance_Rate": 0 } } }
TB_Drug_Efficacy_Multiplier_Failed	float	0	1	1	The scale factor for drug efficacy parameters (TB_Drug_Inactivation_Rate and TB_Drug_Clearance_Rate) to give the efficacy of first-line drugs when given to individuals who have failed treatment.	{ "TB_Drug_Efficacy_Multiplier_Failed": 1.0 }
TB_Drug_Efficacy_Multiplier_MDR	float	0	1	1	The scale factor for drug efficacy parameters (TB_Drug_Inactivation_Rate and TB_Drug_Clearance_Rate) to give the efficacy of first-line drugs when given to individuals with MDR-TB.	{ "TB_Drug_Efficacy_Multiplier_MDR": 1.0 }
TB_Drug_Efficacy_Multiplier_Relapsed	float	0	1	1	The scale factor for drug efficacy parameters (TB_Drug_Inactivation_Rate and TB_Drug_Clearance_Rate) to give the efficacy of first-line drugs when given to individuals who have relapsed after treatment.	{ "TB_Drug_Efficacy_Multiplier_Relapsed": 1.0 }
TB_Drug_Inactivation_Rate	float	0	1	1	The daily rate at which treatment with an anti-TB drug causes inactivation. This parameter, TB_Drug_Clearance_Rate, TB_Drug_Mortality_Rate, and TB_Drug_Relapse_Rate specify the daily probability of a change in the TB disease state during the course of treatment. The sum of these rates and the total duration of treatment specify the probability that any event occurs. If no event occurs, the individual fails drug treatment.	{ "TB_Drug_Params": { "BurninDrug": { "TB_Drug_Clearance_Rate": 0.0111, "TB_Drug_Inactivation_Rate": 0, "TB_Drug_Mortality_Rate": 0.00162, "TB_Drug_Relapse_Rate": 0.00223, "TB_Drug_Resistance_Rate": 0 } } }
TB_Drug_Mortality_Rate	float	0	1	0	The daily rate at which treatment with an anti-TB drug causes death. This parameter, TB_Drug_Inactivation_Rate, TB_Drug_Relapse_Rate, and TB_Drug_Clearance_Rate specify the daily probability of a change in the TB disease state during the course of treatment. The sum of these rates and the total duration of treatment specify the probability that any event occurs. If no event occurs, the individual fails drug treatment.	{ "TB_Drug_Params": { "BurninDrug": { "TB_Drug_Clearance_Rate": 0.0111, "TB_Drug_Inactivation_Rate": 0, "TB_Drug_Mortality_Rate": 0.00162, "TB_Drug_Relapse_Rate": 0.00223, "TB_Drug_Resistance_Rate": 0 } } }
TB_Drug_Params	JSON object	NA	NA	NA	This JSON structure contains the names of anti-tuberculosis drugs and the parameters that define them.	{ "TB_Drug_Params":{ "CDCDrug":{ "TB_Drug_Clearance_Rate":0.1, "TB_Drug_Inactivation_Rate":1e-09, "TB_Drug_Mortality_Rate":1e-09, "TB_Drug_Primary_Decay_Time_Constant":180.0, "TB_Drug_Relapse_Rate":1e-09, "TB_Drug_Resistance_Rate":1e-09 }, "CDCDrugMDR":{ "TB_Drug_Clearance_Rate":1e-09, "TB_Drug_Inactivation_Rate":1e-09, "TB_Drug_Mortality_Rate":1e-09, "TB_Drug_Primary_Decay_Time_Constant":180.0, "TB_Drug_Relapse_Rate":1e-09, "TB_Drug_Resistance_Rate":1e-09 } } }
TB_Drug_Primary_Decay_Time_Constant	float	0	100000	0	The number of days before the efficacy of a specific anti-TB drug begins to decay.	{ "TB_Drug_Params": { "CDCDrug": { "TB_Drug_Clearance_Rate": 0.1, "TB_Drug_Inactivation_Rate": 1e-09, "TB_Drug_Mortality_Rate": 1e-09, "TB_Drug_Primary_Decay_Time_Constant": 180.0, "TB_Drug_Relapse_Rate": 1e-09, "TB_Drug_Resistance_Rate": 1e-09 } } }
TB_Drug_Relapse_Rate	float	0	1	0	The daily rate at which treatment with an anti-TB drug causes disease relapse. This parameter, TB_Drug_Inactivation_Rate, TB_Drug_Mortality_Rate, and TB_Drug_Clearance_Rate specify the daily probability of a change in the TB disease state during the course of treatment. The sum of these rates and the total duration of treatment specify the probability that any event occurs. If no event occurs, the individual fails drug treatment.	{ "TB_Drug_Params": { "CDCDrug": { "TB_Drug_Clearance_Rate": 0.1, "TB_Drug_Inactivation_Rate": 1e-09, "TB_Drug_Mortality_Rate": 1e-09, "TB_Drug_Primary_Decay_Time_Constant": 180.0, "TB_Drug_Relapse_Rate": 1e-09, "TB_Drug_Resistance_Rate": 1e-09 } } }
TB_Drug_Resistance_Rate	float	0	1	0	The daily probability that an individual with drug-sensitive TB will acquire MDR-TB. Only individuals who return to the latent state or fail can acquire MDR-TB.	{ "TB_Drug_Params": { "CDCDrug": { "TB_Drug_Clearance_Rate": 0.1, "TB_Drug_Inactivation_Rate": 1e-09, "TB_Drug_Mortality_Rate": 1e-09, "TB_Drug_Primary_Decay_Time_Constant": 180.0, "TB_Drug_Relapse_Rate": 1e-09, "TB_Drug_Resistance_Rate": 1e-09 } } }
TB_Drug_Types_For_This_Sim	array of strings	NA	NA	[]	The list of drug types that will be used with the AntiTBPropDepDrug intervention. The values for each drug type are defined by TB_Drug_Params.	{ "TB_Drug_Types_For_This_Sim": [ "CDCDrug", "CDCDrugRetx", "CDCDrugMDR" ] }

Enable or disable features ¶

The following parameters enable or disable features of the model, such as allowing births, deaths, or aging. Set to false (0) to disable; set to true (1) to enable.

Parameter	Data type	Maximum	Default	Description	Example
Enable_Aging	boolean	1	1	Controls whether or not individuals in a population age during the simulation. Enable_Vital_Dynamics must be set to true (1).	{ "Enable_Vital_Dynamics": 1, "Enable_Aging": 1 }
Enable_Air_Migration	boolean	1	0	Controls whether or not migration by air travel will occur. Migration_Model must be set to FIXED_RATE_MIGRATION.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Air_Migration": 1, "Air_Migration_Filename": "../inputs/air_migration.bin" }
Enable_Birth	boolean	1	1	Controls whether or not individuals will be added to the simulation by birth. Enable_Vital_Dynamics must be set to true (1). If you want new individuals to have the same intervention coverage as existing individuals, you must add a BirthTriggeredIV to the campaign file.	{ "Enable_Vital_Dynamics": 1, "Enable_Birth": 1 }
Enable_Climate_Stochasticity	boolean	1	0	Controls whether or not the climate has stochasticity. Climate_Model must be set to CLIMATE_CONSTANT or CLIMATE_BY_DATA. Set the variance using the parameters Air_Temperature_Variance, Land_Temperature_Variance, Enable_Rainfall_Stochasticity, and Relative_Humidity_Variance.	{ "Climate_Model": "CLIMATE_BY_DATA", "Enable_Climate_Stochasticity": 1, "Air_Temperature_Variance": 2, "Enable_Rainfall_Stochasticity": 1, "Land_Temperature_Variance": 2, "Relative_Humidity_Variance": 0.05 }
Enable_Default_Reporting	boolean	1	1	Controls whether or not the default InsetChart.json report is created.	{ "Enable_Default_Reporting": 1 }
Enable_Demographics_Birth	boolean	1	0	Controls whether or not newborns have identical or heterogeneous characteristics. Set to false (0) to give all newborns identical characteristics; set to true (1) to allow for heterogeneity in traits such as sickle-cell status. Enable_Birth must be set to true (1).	{ "Enable_Birth": 1, "Enable_Demographics_Birth": 1 }
Enable_Demographics_Builtin	boolean	1	0	Controls whether or not built-in demographics for default geography will be used. Note that the built-in demographics feature does not represent a real geographical location and is mostly used for testing. Set to true (1) to define the initial population and number of nodes using Default_Geography_Initial_Node_Population and Default_Geography_Torus_Size. Set to false (0) to use demographics input files defined in Demographics_Filenames.	{ "Enable_Demographics_Builtin": 1, "Default_Geography_Initial_Node_Population": 1000, "Default_Geography_Torus_Size": 3 }
Enable_Demographics_Gender	boolean	1	1	Controls whether or not gender ratios are drawn from a Gaussian or 50/50 draw. Set to true (1) to create gender ratios drawn from a male/female ratio that is randomly smeared by a Gaussian of width 1%; set to false (0) to assign a gender ratio based on a 50/50 draw.	{ "Enable_Demographics_Gender": 1 }
Enable_Demographics_Reporting	boolean	1	1	Controls whether or not demographic summary data and age-binned reports are outputted to file.	{ "Enable_Demographics_Reporting": 1 }
Enable_Demographics_Risk	boolean	1	0	Controls whether or not the simulation includes the impact of disease risk in demographics. This parameter is not used in this simulation type.	{ "Enable_Demographics_Risk": 1 }
Enable_Disease_Mortality	boolean	1	1	Controls whether or not individuals are removed from the simulation due to disease deaths. This parameter is not used with this simulation type.	{ "Enable_Disease_Mortality": 1 }
Enable_Family_Migration	boolean	1	0	Controls whether or not all members of a household can migrate together. All residents must be home before they can leave on the trip. Migration_Model must be set to FIXED_RATE_MIGRATION.	{ "Enable_Migration": "FIXED_RATE_MIGRATION", "Enable_Family_Migration": 1, "Family_Migration_Filename": "../inputs/family_migration.bin" }
Enable_Heterogeneous_Intranode_Transmission	boolean	1	0	Controls whether or not individuals experience heterogeneous disease transmission within a node. When set to true (1), individual property definitions and the $\beta$ matrix must be specified in the demographics file (see NodeProperties and IndividualProperties parameters). The $\beta$ values are multiplied with the $\beta$ ₀ value configured by Base_Infectivity. This is used only in generic simulations, but must be set to false (0) for all other simulation types. Heterogeneous transmission for other diseases uses other mechanistic parameters included with the simulation type.	{ "Enable_Heterogeneous_Intranode_Transmission": 1 }
Enable_Immune_Decay	boolean	1	1	Controls whether or not immunity decays after an infection clears. Set to true (1) if immunity decays; set to false (0) if recovery from the disease confers complete immunity for life. Enable_Immunity must be set to true (1).	{ "Enable_Immunity": 1, "Enable_Immune_Decay": 0 }
Enable_Immunity	boolean	1	1	Controls whether or not an individual has protective immunity after an infection clears.	{ "Enable_Immunity": 1 }
Enable_Immunity_Distribution	boolean	1	0	Controls whether or not individuals in the population have immunity at the beginning of the simulation. If set to 0, individuals are not initialized with immunity but may acquire immunity. If set to 1, you must indicate the type of distribution to use for immunity in the configuration parameter Immunity_Initialization_Distribution_Type and the distribution values in the demographics file. Enable_Immunity must be set to 1.	{ "Enable_Immunity": 1, "Enable_Immunity_Distribution": 1, "Immunity_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE" }
Enable_Initial_Prevalence	boolean	1	0	Controls whether or not parameters in the demographics file are used to define a distribution for the number of infected people per node at the beginning of the simulation. Set the distribution under NodeAttributes using PrevalenceDistributionFlag, PrevalenceDistribution1, and PrevalenceDistribution2. This parameter is not used in this simulation type.	{ "Enable_Initial_Prevalence": 1 }
Enable_Interventions	boolean	1	0	Controls whether or not campaign interventions will be used in the simulation. Set Campaign_Filename to the path of the file that contains the campaign interventions.	{ "Enable_Interventions": 1, "Campaign_Filename": "campaign.json" }
Enable_Local_Migration	boolean	1	0	Controls whether or not local migration (the diffusion of people in and out of nearby nodes by foot travel) occurs. Migration_Model must be set to FIXED_RATE_MIGRATION.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Local_Migration": 1, "Local_Migration_Filename": "../inputs/local_migration.bin" }
Enable_Maternal_Infection_Transmission	boolean	1	0	Controls whether or not infectious mothers infect infants at birth. Enable_Birth must be set to 1 (true).	{ "Enable_Birth": 1, "Enable_Maternal_Infection_Transmission": 1 }
Enable_Maternal_Protection	boolean	1	0	Controls whether or not mothers pass immunity on to their infants. Setting to 1 (true) enables maternal protection as defined by Maternal_Protection_Type. Enable_Birth must be set to 1 (true).	{ "Enable_Maternal_Protection": 1, "Maternal_Protection_Type": "LINEAR_FRACTIONAL" }
Enable_Migration_Heterogeneity	boolean	1	1	Controls whether or not migration rate is heterogeneous among individuals. Set to true (1) to use a migration rate distribution in the demographics file (see NodeAttributes parameters); set to false (0) to use the same migration rate applied to all individuals. Migration_Model must be set to FIXED_RATE_MIGRATION.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Migration_Heterogeneity": 1 }
Enable_Natural_Mortality	boolean	1	0	Controls whether or not individuals are removed from the simulation due to natural (non-disease) deaths. Enable_Vital_Dynamics must be set to 1 (true).	{ "Enable_Natural_Mortality": 1, "Enable_Vital_Dynamics": 1 }
Enable_Property_Output	boolean	1	0	Controls whether or not to create property output reports, which detail groups as defined in IndividualProperties in the demographics file (see NodeProperties and IndividualProperties parameters). When there is more than one property type, the report will display the channel information for all combinations of the property type groups.	{ "Enable_Property_Output": 1 }
Enable_Rainfall_Stochasticity	boolean	1	1	Controls whether or not there is stochastic variation in rainfall; set to true (1) for stochastic variation of rainfall that is drawn from an exponential distribution (with a mean value as the daily rainfall from the Climate_Model values CLIMATE_CONSTANT or CLIMATE_BY_DATA), or set to false (0) to disable rainfall stochasticity.	{ "Enable_Climate_Stochasticity": 1, "Air_Temperature_Variance": 2, "Enable_Rainfall_Stochasticity": 1, "Land_Temperature_Variance": 2, "Relative_Humidity_Variance": 0.05 }
Enable_Regional_Migration	boolean	1	0	Controls whether or not there is migration by road vehicle into and out of nodes in the road network. Migration_Model must be set to FIXED_RATE_MIGRATION.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Regional_Migration": 1, "Regional_Migration_Filename": "../inputs/regional_migration.bin" }
Enable_Sea_Migration	boolean	1	0	Controls whether or not there is migration on ships into and out of coastal cities with seaports. Migration_Model must be set to FIXED_RATE_MIGRATION.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Sea_Migration": 1, "Sea_Migration_Filename": "../inputs/sea_migration.bin" }
Enable_Spatial_Output	boolean	1	0	Controls whether or not spatial output reports are created. If set to true (1), spatial output reports include all channels listed in the parameter Spatial_Output_Channels. Note Spatial output files require significant processing time and disk space.	{ "Enable_Spatial_Output": 1, "Spatial_Output_Channels": [ "Prevalence", "New_Infections" ] }
Enable_Superinfection	boolean	1	0	Controls whether or not an individual can have multiple infections simultaneously. Set to true (1) to allow for multiple simultaneous infections; set to false (0) if multiple infections are not possible. Set the Max_Individual_Infections parameter.	{ "Enable_Superinfection": 1, "Max_Individual_Infections": 2 }
Enable_Susceptibility_Scaling	boolean	1	0	Controls whether or not susceptibility is scaled by time as defined by Susceptibility_Scaling_Type.	{ "Enable_Susceptibility_Scaling": 1, "Susceptibility_Scaling_Type": "LOG_LINEAR_FUNCTION_OF_TIME" }
Enable_Vital_Dynamics	boolean	1	1	Controls whether or not births and deaths occur in the simulation. Births and deaths must be individually enabled and set.	{ "Enable_Vital_Dynamics":1, "Enable_Birth": 1, "Death_Rate_Dependence": "NONDISEASE_MORTALITY_OFF", "Base_Mortality": 0.002 }

General disease ¶

The following parameters determine general disease characteristics.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Animal_Reservoir_Type	enum	NA	NA	NO_ZOONOSIS	The type of animal reservoir and configuration for zoonosis. Use of the animal reservoir sets a low constant baseline of infectivity beyond what is present in the human population. It allows a more random introduction of cases in continuous time, which is more applicable for various situations such as zoonosis. Possible values are: NO_ZOONOSIS There is no animal reservoir. CONSTANT_ZOONOSIS The daily rate of zoonotic infection is configured by the parameter Zoonosis_Rate. ZOONOSIS_FROM_DEMOGRAPHICS The zoonosis rate is scaled first by the node-specific Zoonosis value in the demographics file (see NodeAttributes parameters). If Zoonosis is not configured, the simulation will then use the value for Zoonosis_Rate.	{ "Animal_Reservoir_Type": "CONSTANT_ZOONOSIS" }
Number_Basestrains	integer	1	10	1	The number of base strains in the simulation, such as antigenic variants.	{ "Number_Basestrains": 1 }
Number_Substrains	integer	1	16777200	256	The number of disease substrains for each base strain, such as genetic variants.	{ "Number_Substrains": 16777216 }
Zoonosis_Rate	float	0	1	0	The daily rate of zoonotic infection per individual. Animal_Reservoir_Type must be set to CONSTANT_ZOONOSIS or ZOONOSIS_FROM_DEMOGRAPHICS. If Animal_Reservoir_Type is set to ZOONOSIS_FROM_DEMOGRAPHICS, the value for the Zoonosis NodeAttribute in the demographics file will override the value set for Zoonosis_Rate.	{ "Zoonosis_Rate": 0.005 }

Geography and environment ¶

The following parameters determine characteristics of the geography and environment of the simulation. For example, how to use the temperature or rainfall data in the climate files and the size of the nodes in the simulation.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Air_Temperature_Filename	string	NA	NA	air_temp.json	The path to the input data file that defines air temperature data measured two meters above ground. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format.	{ "Climate_Model": "CLIMATE_BY_DATA", "Air_Temperature_Filename": "Namawala_single_node_air_temperature_daily.bin" }
Air_Temperature_Offset	float	-20	20	0	The linear shift of air temperature in degrees Celsius. Climate_Model must be set to CLIMATE_BY_DATA.	{ "Air_Temperature_Offset": 1 }
Air_Temperature_Variance	float	0	5	2	The standard deviation (in degrees Celsius) for normally distributed noise applied to the daily air temperature values when Climate_Model is configured as CLIMATE_CONSTANT or CLIMATE_BY_DATA. Enable_Climate_Stochasticity must be set to true (1).	{ "Enable_Climate_Stochasticity": 1, "Air_Temperature_Variance": 2, }
Base_Air_Temperature	float	-55	45	22	The air temperature, in degrees Celsius, where Climate_Model is set to CLIMATE_CONSTANT.	{ "Climate_Model": "CLIMATE_CONSTANT", "Base_Air_Temperature": 30 }
Base_Land_Temperature	float	-55	60	26	The land temperature, in degrees Celsius, where Climate_Model is set to CLIMATE_CONSTANT.	{ "Climate_Model": "CLIMATE_CONSTANT", "Base_Land_Temperature": 20 }
Base_Rainfall	float	0	150	10	The value of rainfall per day in millimeters when Climate_Model is set to CLIMATE_CONSTANT.	{ "Climate_Model": "CLIMATE_CONSTANT", "Base_Rainfall": 20 }
Base_Relative_Humidity	float	0	1	0.75	The value of humidity where Climate_Model is set to CLIMATE_CONSTANT.	{ "Base_Relative_Humidity": 0.1 }
Climate_Model	enum	NA	NA	CLIMATE_OFF	How and from what files the climate of a simulation is configured. The possible values are: CLIMATE_OFF No climate files used. CLIMATE_CONSTANT Uses the conditional parameters that give the fixed values of temperature or rain for land temperature, air temperature, rainfall, and humidity. CLIMATE_KOPPEN Uses an input data file that decodes Koppen codes by geographic location. CLIMATE_BY_DATA Reads everything out of several input data files with additional parameters that allow the addition of stochasticity or scale offsets.	{ "Climate_Model": "CLIMATE_CONSTANT" }
Climate_Update_Resolution	enum	NA	NA	CLIMATE_UPDATE_YEAR	The resolution of data in climate files. Climate_Model must be set to CLIMATE_CONSTANT, CLIMATE_BY_DATA, or CLIMATE_KOPPEN. Possible values are: CLIMATE_UPDATE_YEAR CLIMATE_UPDATE_MONTH CLIMATE_UPDATE_WEEK CLIMATE_UPDATE_DAY CLIMATE_UPDATE_HOUR	{ "Climate_Update_Resolution": "CLIMATE_UPDATE_DAY" }
Default_Geography_Initial_Node_Population	integer	0	1000000	1000	When using the built-in demographics for default geography, the initial number of individuals in each node. Note that the built-in demographics feature does not represent a real geographical location and is mostly used for testing. Enable_Demographics_Builtin must be set to true (1).	{ "Enable_Demographics_Builtin": 1, "Default_Geography_Initial_Node_Population": 1000, "Default_Geography_Torus_Size": 3 }
Default_Geography_Torus_Size	integer	3	100	10	When using the built-in demographics for default geography, the square root of the number of nodes in the simulation. The simulation uses an N x N square grid of nodes with N specified by this parameter. If migration is enabled, the N x N nodes are assumed to be a torus and individuals can migrate from any node to all four adjacent nodes. To enable migration, set Migration_Model to FIXED_RATE_MIGRATION. Built-in migration is a form of “local” migration where individuals only migrate to the adjacent nodes. You can use the x_Local_Migration parameter to control the rate of migration. The other migration parameters are ignored. Note that the built-in demographics feature does not represent a real geographical location and is mostly used for testing. Enable_Demographics_Builtin must be set to true (1).	{ "Enable_Demographics_Builtin": 1, "Default_Geography_Initial_Node_Population": 1000, "Default_Geography_Torus_Size": 3 }
Enable_Climate_Stochasticity	boolean	0	1	0	Controls whether or not the climate has stochasticity. Climate_Model must be set to CLIMATE_CONSTANT or CLIMATE_BY_DATA. Set the variance using the parameters Air_Temperature_Variance, Land_Temperature_Variance, Enable_Rainfall_Stochasticity, and Relative_Humidity_Variance.	{ "Climate_Model": "CLIMATE_BY_DATA", "Enable_Climate_Stochasticity": 1, "Air_Temperature_Variance": 2, "Enable_Rainfall_Stochasticity": 1, "Land_Temperature_Variance": 2, "Relative_Humidity_Variance": 0.05 }
Enable_Rainfall_Stochasticity	boolean	0	1	1	Controls whether or not there is stochastic variation in rainfall; set to true (1) for stochastic variation of rainfall that is drawn from an exponential distribution (with a mean value as the daily rainfall from the Climate_Model values CLIMATE_CONSTANT or CLIMATE_BY_DATA), or set to false (0) to disable rainfall stochasticity.	{ "Enable_Climate_Stochasticity": 1, "Air_Temperature_Variance": 2, "Enable_Rainfall_Stochasticity": 1, "Land_Temperature_Variance": 2, "Relative_Humidity_Variance": 0.05 }
Koppen_Filename	string	NA	NA	koppen_climate.json	The path to the input file used to specify Koppen climate classifications. Climate_Model must be set to CLIMATE_KOPPEN.	{ "Koppen_Filename": "Mad_2_5arcminute_koppen.dat" }
Land_Temperature_Filename	string	NA	NA	land_temp.json	The path of the input file defining temperature data measured at land surface; used only when Climate_Model is set to CLIMATE_BY_DATA. The file must be in .bin format.	{ "Land_Temperature_Filename": "Namawala_single_node_land_temperature_daily.bin" }
Land_Temperature_Offset	float	-20	20	0	The linear shift of land surface temperature in degrees Celsius; only used when Climate_Model is set to CLIMATE_BY_DATA.	{ "Land_Temperature_Offset": 0 }
Land_Temperature_Variance	float	0	7	2	The standard deviation (in degrees Celsius) for normally distributed noise applied to the daily land temperature values when Climate_Model is configured to CLIMATE_CONSTANT or CLIMATE_BY_DATA; only used if the Enable_Climate_Stochasticity is set to true (1).	{ "Land_Temperature_Variance": 1.5 }
Node_Grid_Size	float	0.00416	90	0.004167	The spatial resolution indicating the node grid size for a simulation in degrees.	{ "Node_Grid_Size": 0.042 }
Rainfall_Filename	string	NA	NA	rainfall.json	The path of the input file which defines rainfall data. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format.	{ "Rainfall_Filename": "Namawala_single_node_rainfall_daily.bin" }
Rainfall_Scale_Factor	float	0.1	10	1	The scale factor used in multiplying rainfall value(s). Climate_Model must be set to CLIMATE_BY_DATA.	{ "Rainfall_Scale_Factor": 1 }
Relative_Humidity_Filename	string	NA	NA	rel_hum.json	The path of the input file which defines relative humidity data measured 2 meters above ground. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format.	{ "Relative_Humidity_Filename": "Namawala_single_node_relative_humidity_daily.bin" }
Relative_Humidity_Scale_Factor	float	0.1	10	1	The scale factor used in multiplying relative humidity values. Climate_Model must be set to CLIMATE_BY_DATA.	{ "Relative_Humidity_Scale_Factor": 1 }
Relative_Humidity_Variance	float	0	0.12	0.05	The standard deviation (in percentage) for normally distributed noise applied to the daily relative humidity values when Climate_Model is configured as CLIMATE_CONSTANT or CLIMATE_BY_DATA. Enable_Climate_Stochasticity must be set to true (1).	{ "Relative_Humidity_Variance": 0.05 }

Immunity ¶

The following parameters determine the immune system response for the disease being modeled, including waning immunity after an infection clears.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Acquisition_Blocking_Immunity_Decay_Rate	float	0	1000	0.001	The rate at which acquisition-blocking immunity decays after the initial period indicated by the base acquisition-blocking immunity offset. Only used when Enable_Immunity and Enable_Immune_Decay parameters are set to true (1).	{ "Acquisition_Blocking_Immunity_Decay_Rate": 0.05 }
Acquisition_Blocking_Immunity_Duration_Before_Decay	float	0	45000	0	The number of days after infection until acquisition-blocking immunity begins to decay. Enable_Immunity and Enable_Immune_Decay must be set to true (1).	{ "Acquisition_Blocking_Immunity_Duration_Before_Decay": 10 }
Enable_Immune_Decay	boolean	0	1	1	Controls whether or not immunity decays after an infection clears. Set to true (1) if immunity decays; set to false (0) if recovery from the disease confers complete immunity for life. Enable_Immunity must be set to true (1).	{ "Enable_Immunity": 1, "Enable_Immune_Decay": 0 }
Enable_Immunity	boolean	0	1	1	Controls whether or not an individual has protective immunity after an infection clears.	{ "Enable_Immunity": 1 }
Enable_Immunity_Distribution	boolean	0	1	0	Controls whether or not individuals in the population have immunity at the beginning of the simulation. If set to 0, individuals are not initialized with immunity but may acquire immunity. If set to 1, you must indicate the type of distribution to use for immunity in the configuration parameter Immunity_Initialization_Distribution_Type and the distribution values in the demographics file. Enable_Immunity must be set to 1.	{ "Enable_Immunity": 1, "Enable_Immunity_Distribution": 1, "Immunity_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE" }
Enable_Maternal_Protection	boolean	0	1	0	Controls whether or not mothers pass immunity on to their infants. Setting to 1 (true) enables maternal protection as defined by Maternal_Protection_Type. Enable_Birth must be set to 1 (true).	{ "Enable_Maternal_Protection": 1, "Maternal_Protection_Type": "LINEAR_FRACTIONAL" }
Immune_Threshold_For_Downsampling	float	0	1	0	Threshold on acquisition immunity at which to apply immunity dependent downsampling. Individual_Sampling_Type must set to ADAPTED_SAMPLING_BY_IMMUNE_STATE.	{ "Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_IMMUNE_STATE", "Immune_Threshold_For_Downsampling": 0.5 }
Immunity_Acquisition_Factor	float	0	1000	0	The multiplicative reduction in the probability of reacquiring disease. Enable_Immunity must be set to 1 (true).	{ "Enable_Immunity": 1, "Enable_Immune_Decay": 1, "Immunity_Acquisition_Factor": 0.9 }
Immunity_Initialization_Distribution_Type	enum	NA	NA	DISTRIBUTION_OFF	The method for initializing the immunity distribution in the simulated population. Enable_Immunity_Distribution must be set to 1 (true). Possible values are: DISTRIBUTION_OFF All individuals default to no immunity. DISTRIBUTION_SIMPLE Individual immunities are drawn from a distribution whose functional form and parameters are specified in the demographics file in IndividualAttributes using ImmunityDistributionFlag, ImmunityDistribution1, and ImmunityDistribution2 (see Simple distributions parameters). DISTRIBUTION_COMPLEX Individual immunities are drawn from an age-dependent piecewise linear function for each specific antibody in the demographics file (see Complex distributions parameters).	{ "Enable_Immunity": 1, "Enable_Immunity_Distribution": 1, "Immunity_Initialization_Distribution_Type": "DISTRIBUTION_COMPLEX" }
Immunity_Mortality_Factor	float	0	1000	0	The multiplicative reduction in the probability of dying from infection after getting re-infected. Enable_Immunity must be set to 1 (true).	{ "Enable_Immunity": 1, "Enable_Immune_Decay": 1, "Immunity_Mortality_Factor": 0.5 }
Immunity_Transmission_Factor	float	0	1000	0	The multiplicative reduction in the probability of transmitting infection after getting re-infected. Enable_Immunity must be set to 1 (true).	{ "Enable_Immunity": 1, "Enable_Immunity_Decay": 1, "Immunity_Transmission_Factor": 0.9 }
Maternal_Linear_Slope	float	0.0001	1	0.01	Slope parameter describing the rate of waning for maternal protection, must be positive. The per-day increase in susceptibility. Maternal_Protection_Type must be set to LINEAR_FRACTIONAL or LINEAR_BINARY.	{ "Maternal_Protection_Type": "LINEAR_FRACTIONAL", "Maternal_Linear_SusZero": 0.45, "Maternal_Linear_Slope": 0.02 }
Maternal_Linear_SusZero	float	0	1	0.2	The initial level of maternal protection at age 0, given as susceptibility. A value of 0.0 implies total protection, a value of 1.0 implies no protection. Maternal_Protection_Type must be set to LINEAR_FRACTIONAL or LINEAR_BINARY.	{ "Maternal_Protection_Type": "LINEAR_FRACTIONAL", "Maternal_Linear_SusZero": 0.45, "Maternal_Linear_Slope": 0.02 }
Maternal_Protection_Type	enum	NA	NA	NONE	The type of maternal protection afforded to infants. Enable_Maternal_Protection must be set to 1 (true). Possible values are: NONE No immune protection is passed on to infants. LINEAR_FRACTIONAL Susceptibility is a function of age and equal susceptibility is assigned to all individuals. Susceptibility = Maternal_Linear_Slope * age + Maternal_Linear_SusZero LINEAR_BINARY Individuals are assigned an age cutoff before which they have reduced susceptibility and after which they do not. The age cutoff is randomly assigned to each individual. AgeCutoff = (RAND - Maternal_Linear_SusZero) / Maternal_Linear_Slope SIGMOID_FRACTIONAL Susceptibility is a function of age and equal susceptibility is assigned to all individuals. Susceptibility = Maternal_Sigmoid_SusInit + (1.0 - Maternal_Sigmoid_SusInit) / * (1.0 + EXP(( Maternal_Sigmoid_HalfMaxAge - age) / Maternal_Sigmoid_SteepFac)) SIGMOID_BINARY Individuals are assigned an age cutoff before which they have reduced susceptibility and after which they do not. The age cutoff is randomly assigned to each individual. AgeCutoff = ( Maternal_Sigmoid_HalfMaxAge - Maternal_Sigmoid_SteepFac * LOG (1.0 / RAND - 1.0 + 0.001)	{ "Enable_Maternal_Protection": 1, "Maternal_Protection_Type": "LINEAR_FRACTIONAL" }
Maternal_Sigmoid_HalfMaxAge	float	-270	3650	180	The age in days that the level of maternal protection is half of its initial value. Maternal_Protection_Type must be set to SIGMOID_FRACTIONAL or SIGMOID_BINARY.	{ "Maternal_Protection_Type": "SIGMOID_BINARY", "Maternal_Sigmoid_SteepFac": 30, "Maternal_Sigmoid_HalfMaxAge": 365, "Maternal_Sigmoid_SusInit": 0.0002 }
Maternal_Sigmoid_SteepFac	float	0.1	1000	30	The steepness factor describing the rate of waning for maternal protection, must be positive. Small values imply rapid waning.Maternal_Protection_Type must be set to SIGMOID_FRACTIONAL or SIGMOID_BINARY.	{ "Maternal_Protection_Type": "SIGMOID_BINARY", "Maternal_Sigmoid_SteepFac": 30, "Maternal_Sigmoid_HalfMaxAge": 365, "Maternal_Sigmoid_SusInit": 0.0002 }
Maternal_Sigmoid_SusInit	float	0	1	0	The initial level of maternal protection at age -INF, given as susceptibility. A value of 0.0 implies total protection, a value of 1.0 implies no protection. Maternal_Protection_Type must be set to SIGMOID_FRACTIONAL or SIGMOID_BINARY.	{ "Maternal_Protection_Type": "SIGMOID_BINARY", "Maternal_Sigmoid_SteepFac": 30, "Maternal_Sigmoid_HalfMaxAge": 365, "Maternal_Sigmoid_SusInit": 0.0002 }
Mortality_Blocking_Immunity_Decay_Rate	float	0	1000	0.001	The rate at which mortality-blocking immunity decays after the mortality-blocking immunity offset period. Enable_Immune_Decay must be set to 1.	{ "Mortality_Blocking_Immunity_Decay_Rate": 0.1 }
Mortality_Blocking_Immunity_Duration_Before_Decay	float	0	45000	0	The number of days after infection until mortality-blocking immunity begins to decay. Enable_Immunity and Enable_Immune_Decay must be set to 1.	{ "Mortality_Blocking_Immunity_Duration_Before_Decay": 270 }
TB_Immune_Loss_Fraction	float	0	1	0.5	The fraction of individuals that lose immunity with time.	{ "TB_Immune_Loss_Fraction": 0 }
Transmission_Blocking_Immunity_Decay_Rate	float	0	1000	0.001	The rate at which transmission-blocking immunity decays after the base transmission-blocking immunity offset period. Used only when Enable_Immunity and Enable_Immune_Decay parameters are set to true (1).	{ "Transmission_Blocking_Immunity_Decay_Rate": 0.01 }
Transmission_Blocking_Immunity_Duration_Before_Decay	float	0	45000	0	The number of days after infection until transmission-blocking immunity begins to decay. Only used when Enable_Immunity and Enable_Immune_Decay parameters are set to true (1).	{ "Transmission_Blocking_Immunity_Duration_Before_Decay": 90 }

Incubation ¶

The following parameters determine the characteristics of the incubation period.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Base_Incubation_Period	float	0	3.40E+38	6	Average duration, in days, of the incubation period before infected individuals become infectious. Incubation_Period_Distribution must be set to either FIXED_DURATION or EXPONENTIAL_DURATION.	{ "Incubation_Period_Distribution": "EXPONENTIAL_DURATION", "Base_Incubation_Period": 1 }
Incubation_Period_Distribution	enum	NA	NA	NOT_INITIALIZED	The distribution for the duration of the incubation period. Possible values are: NOT_INITIALIZED No distribution set. FIXED_DURATION Base_Incubation_Period is the constant duration incubation period. UNIFORM_DURATION Incubation_Period_Min and Incubation_Period_Max define the ranges of a uniform random draw for the duration of the incubation period. GAUSSIAN_DURATION Incubation_Period_Mean and Incubation_Period_Std_Dev define the mean and standard deviation of the Gaussian from which the incubation period is drawn. Negative values are truncated at zero. EXPONENTIAL_DURATION Base_Incubation_Period is the mean of the exponential random draw. POISSON_DURATION Incubation_Period_Mean is the mean of the random Poisson draw. LOG_NORMAL_DURATION Incubation_Period_Log_Mean is the mean of log normal and Incubation_Period_Log_Width is the log width of log normal. BIMODAL_DURATION The distribution is bimodal, the duration is a fraction of Base_Incubation_Period for a specified period of time and equal to Base_Incubation_Period otherwise. PIECEWISE_CONSTANT The distribution is specified with a list of years and a matching list of values. The duration at a given year is that specified for the nearest previous year. PIECEWISE_LINEAR The distribution is specified with a list of years and matching list of values. The duration at a given year is a linear interpolation of the specified values. WEIBULL_DURATION The duration is a Weibull distribution with a given scale and shape. DUAL_TIMESCALE_DURATION The duration is two exponential distributions with given means.	{ "Base_Incubation_Period": 5, "Incubation_Period_Distribution": "EXPONENTIAL_DURATION" }
Incubation_Period_Log_Mean	float	0	3.40E+38	6	The mean of log normal for the incubation period distribution. Incubation_Period_Distribution must be set to LOG_NORMAL_DURATION.	{ "Incubation_Period_Distribution": "LOG_NORMAL_DURATION", "Incubation_Period_Log_Mean": 5.758, "Incubation_Period_Log_Width": 0.27 }
Incubation_Period_Log_Width	float	0	3.40E+38	1	The log width of log normal for the incubation period distribution. Incubation_Period_Distribution must be set to LOG_NORMAL_DURATION.	{ "Incubation_Period_Distribution": "LOG_NORMAL_DURATION", "Incubation_Period_Log_Mean": 5.758, "Incubation_Period_Log_Width": 0.27 }
Incubation_Period_Max	float	0.6	3.40E+38	0	The maximum length of the incubation period. Incubation_Period_Distribution must be set to UNIFORM_DURATION.	{ "Incubation_Period_Distribution": "UNIFORM_DURATION", "Incubation_Period_Min": 2, "Incubation_Period_Max": 6 }
Incubation_Period_Mean	float	0	3.40E+38	6	The mean of the incubation period. Incubation_Period_Distribution must be set to either GAUSSIAN_DURATION or POISSON_DURATION.	{ "Incubation_Period_Distribution": "GAUSSIAN_DURATION", "Incubation_Period_Mean": 7, "Infectious_Period_Std_Dev": 2 }
Incubation_Period_Min	float	0	3.40E+38	0	The minimum length of the incubation period. Incubation_Period_Distribution must be set to UNIFORM_DURATION.	{ "Incubation_Period_Distribution": "UNIFORM_DURATION", "Incubation_Period_Min": 2, "Incubation_Period_Max": 6 }
Incubation_Period_Std_Dev	float	0	3.40E+38	1	The standard deviation incubation period. Incubation_Period_Distribution must be set to GAUSSIAN_DURATION.	{ "Incubation_Period_Distribution": "GAUSSIAN_DURATION", "Incubation_Period_Mean": 7, "Infectious_Period_Std_Dev": 2 }

Infectivity and transmission ¶

The following parameters determine aspects of infectivity and disease transmission. For example, how infectious individuals are and the length of time for which they remain infectious, whether the disease can be maternally transmitted, and how population density affects infectivity.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Base_Infectious_Period	float	0	3.40E+38	6	Average duration, in days, of the infectious period before the infection is cleared. Infectious_Period_Distribution must be set to either FIXED_DURATION or EXPONENTIAL_DURATION.	{ "Base_Infectious_Period": 4 }
Base_Infectivity	float	0	1000	0.3	The base infectiousness of individuals before accounting for transmission-blocking effects of acquired immunity and/or campaign interventions.	{ "Base_Infectivity": 0.5 }
Enable_Heterogeneous_Intranode_Transmission	boolean	0	1	0	Controls whether or not individuals experience heterogeneous disease transmission within a node. When set to true (1), individual property definitions and the $\beta$ matrix must be specified in the demographics file (see NodeProperties and IndividualProperties parameters). The $\beta$ values are multiplied with the $\beta$ ₀ value configured by Base_Infectivity. This is used only in generic simulations, but must be set to false (0) for all other simulation types. Heterogeneous transmission for other diseases uses other mechanistic parameters included with the simulation type.	{ "Enable_Heterogeneous_Intranode_Transmission": 1 }
Enable_Initial_Prevalence	boolean	0	1	0	Controls whether or not parameters in the demographics file are used to define a distribution for the number of infected people per node at the beginning of the simulation. Set the distribution under NodeAttributes using PrevalenceDistributionFlag, PrevalenceDistribution1, and PrevalenceDistribution2. This parameter is not used in this simulation type.	{ "Enable_Initial_Prevalence": 1 }
Enable_Maternal_Infection_Transmission	boolean	0	1	0	Controls whether or not infectious mothers infect infants at birth. Enable_Birth must be set to 1 (true).	{ "Enable_Birth": 1, "Enable_Maternal_Infection_Transmission": 1 }
Enable_Skipping	boolean	0	1	0	Controls whether or not the simulation uses an optimization that can increase performance by up to 50% in some cases by probablistically exposing individuals rather than exposing every single person. Useful in low-prevalence, high-population scenarios. This parameter is not used in this simulation type.	{ "Exposure_Skipping": 1 }
Enable_Superinfection	boolean	0	1	0	Controls whether or not an individual can have multiple infections simultaneously. Set to true (1) to allow for multiple simultaneous infections; set to false (0) if multiple infections are not possible. Set the Max_Individual_Infections parameter.	{ "Enable_Superinfection": 1, "Max_Individual_Infections": 2 }
Enable_Susceptibility_Scaling	boolean	0	1	0	Controls whether or not susceptibility is scaled by time as defined by Susceptibility_Scaling_Type.	{ "Enable_Susceptibility_Scaling": 1, "Susceptibility_Scaling_Type": "LOG_LINEAR_FUNCTION_OF_TIME" }
Infection_Updates_Per_Timestep	integer	0	144	1	The number of infection updates executed during each timestep; note that a timestep defaults to one day.	{ "Infection_Updates_Per_Timestep": 1 }
Infectious_Period_Distribution	enum	NA	NA	NOT_INITIALIZED	The distribution of the duration of the infectious period. Possible values are: NOT_INITIALIZED No distribution set. FIXED_DURATION Base_Infectious_Period is the constant-duration infectious period. UNIFORM_DURATION Infectious_Period_Min and Infectious_Period_Max define the ranges of a uniform random draw for the duration of the infectious period. GAUSSIAN_DURATION Infectious_Period_Mean and Infectious_Period_Std_Dev define the mean and standard deviation of the Gaussian from which the infectious period is drawn. Negative values are truncated at zero. EXPONENTIAL_DURATION Base_Infectious_Period is the mean of the exponential random draw. POISSON_DURATION Infectious_Period_Mean is the mean of the random Poisson draw. LOG_NORMAL_DURATION The duration is a log normal distribution defined by a specified mean and log width. BIMODAL_DURATION The distribution is bimodal, the duration is a fraction of Base_Incubation_Period for a specified period of time and equal to Base_Incubation_Period otherwise. PIECEWISE_CONSTANT The distribution is specified with a list of years and a matching list of values. The duration at a given year is that specified for the nearest previous year. PIECEWISE_LINEAR The distribution is specified with a list of years and matching list of values. The duration at a given year is a linear interpolation of the specified values. WEIBULL_DURATION The duration is a Weibull distribution with a given scale and shape. DUAL_TIMESCALE_DURATION The duration is two exponential distributions with given means.	{ "Infectious_Period_Distribution": "EXPONENTIAL_DURATION" }
Infectious_Period_Max	float	0.6	3.40E+38	0	The maximum length of the infectious period; used when Infectious_Period_Distribution is set to UNIFORM_DURATION.	{ "Infectious_Period_Distribution": "UNIFORM_DURATION", "Infectious_Period_Max": 15, "Infectious_Period_Min": 5 }
Infectious_Period_Mean	float	0	3.40E+38	6	The mean of the infectious period; used when Infectious_Period_Distribution is set to either GAUSSIAN_DURATION or POISSON_DURATION.	{ "Infectious_Period_Distribution": "GAUSSIAN_DURATION", "Infectious_Period_Mean": 12, "Infectious_Period_Std_Dev": 10 }
Infectious_Period_Min	float	0	3.40E+38	0	The minimum length of the infectious period; used when Infectious_Period_Distribution is set to UNIFORM_DURATION.	{ "Infectious_Period_Distribution": "UNIFORM_DURATION", "Infectious_Period_Max": 15, "Infectious_Period_Min": 5 }
Infectious_Period_Std_Dev	float	0	3.40E+38	1	The standard deviation of the infectious period; used when Infectious_Period_Distribution is set to GAUSSIAN_DURATION.	{ "Infectious_Period_Distribution": "GAUSSIAN_DURATION", "Infectious_Period_Mean": 12, "Infectious_Period_Std_Dev": 10 }
Infectivity_Boxcar_Forcing_Amplitude	float	0	3.40E+38	0	The fractional increase in R0 during the high-infectivity season when Infectivity_Scale_Type is equal to ANNUAL_BOXCAR_FUNCTION.	{ "Infectivity_Boxcar_Forcing_Amplitude": 0.25, "Infectivity_Boxcar_Forcing_End_Time": 270, "Infectivity_Boxcar_Forcing_Start_Time": 90, "Infectivity_Scale_Type": "ANNUAL_BOXCAR_FUNCTION" }
Infectivity_Boxcar_Forcing_End_Time	float	0	365	0	The end of the high-infectivity season when Infectivity_Scale_Type is equal to ANNUAL_BOXCAR_FUNCTION.	{ "Infectivity_Boxcar_Forcing_Amplitude": 0.25, "Infectivity_Boxcar_Forcing_End_Time": 270, "Infectivity_Boxcar_Forcing_Start_Time": 90, "Infectivity_Scale_Type": "ANNUAL_BOXCAR_FUNCTION" }
Infectivity_Boxcar_Forcing_Start_Time	float	0	365	0	The beginning of the high-infectivity season, in days, when Infectivity_Scale_Type is equal to ANNUAL_BOXCAR_FUNCTION.	{ "Infectivity_Boxcar_Forcing_Amplitude": 0.25, "Infectivity_Boxcar_Forcing_End_Time": 270, "Infectivity_Boxcar_Forcing_Start_Time": 90, "Infectivity_Scale_Type": "ANNUAL_BOXCAR_FUNCTION" }
Infectivity_Exponential_Baseline	float	0	1	0	The scale factor applied to Base_Infectivity at the beginning of a simulation, before the infectivity begins to grow exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.	{ "Infectivity_Exponential_Baseline": 0.1, "Infectivity_Exponential_Delay": 90, "Infectivity_Exponential_Rate": 45, "Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME" }
Infectivity_Exponential_Delay	float	0	3.40E+38	0	The number of days before infectivity begins to ramp up exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.	{ "Infectivity_Exponential_Baseline": 0.1, "Infectivity_Exponential_Delay": 90, "Infectivity_Exponential_Rate": 45, "Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME" }
Infectivity_Exponential_Rate	float	0	3.40E+38	0	The daily rate of exponential growth to approach to full infectivity after the delay set by Infectivity_Exponential_Delay has passed. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.	{ "Infectivity_Exponential_Rate": 45 }
Infectivity_Scale_Type	enum	NA	NA	CONSTANT_INFECTIVITY	A scale factor that allows infectivity to be altered by time or season. Possible values are: CONSTANT_INFECTIVITY No infectivity correction is applied. FUNCTION_OF_TIME_AND_LATITUDE Infectivity is corrected for approximate seasonal forcing. The use of a seasonal infectivity correction is a proxy for the effects of varying climate. From October through March, infectivity increases in the Northern Hemisphere and decreases in the Southern Hemisphere. From April through September, the trend reverses: regions closer to the equator have reduced forcing compared to temperate regions. FUNCTION_OF_CLIMATE Allows infectivity to be modulated by weather directly, for example, relative humidity in airborne simulations or rainfall in waterborne simulations. There is no default climate dependence enabled for generic simulations. EXPONENTIAL_FUNCTION_OF_TIME To facilitate certain burn-in scenarios, infectivity ramps up from zero at the beginning of the simulation according to the functional form, 1-exp(-ratetime), where the rate is specified by the parameter Infectivity_Scaling_Rate*. SINUSOIDAL_FUNCTION_OF_TIME Allows infectivity to be time-dependent, following a sinusoidal shape. ANNUAL_BOXCAR_FUNCTION Allows infectivity to follow a boxcar function, such that it will be equal to zero for an entire time period (e.g. year) except for a single interval in which it is equal to a constant.	{ "Infectivity_Scale_Type": "FUNCTION_OF_CLIMATE" }
Infectivity_Sinusoidal_Forcing_Amplitude	float	0	1	0	Sets the amplitude of sinusoidal variations in Base_Infectivity. Only used when Infectivity_Scale_Type is set to SINUSOIDAL_FUNCTION_OF_TIME.	{ "Infectivity_Scale_Type": "SINUSOIDAL_FUNCTION_OF_TIME", "Infectivity_Sinusoidal_Forcing_Amplitude": 0.1, "Infectivity_Sinusoidal_Forcing_Phase": 0 }
Infectivity_Sinusoidal_Forcing_Phase	float	0	365	0	Sets the phase of sinusoidal variations in Base_Infectivity. Only used when Infectivity_Scale_Type is set to SINUSOIDAL_FUNCTION_OF_TIME.	{ "Infectivity_Scale_Type": "SINUSOIDAL_FUNCTION_OF_TIME", "Infectivity_Sinusoidal_Forcing_Amplitude": 0.1, "Infectivity_Sinusoidal_Forcing_Phase": 0 }
Maternal_Infection_Transmission_Probability	float	0	1	0	The probability of transmission of infection from mother to infant at birth. Enable_Maternal_Infection_Transmission must be set to 1. Note For malaria and vector simulations, set this to 0. Instead, use the Maternal_Antibody_Protection, Maternal_Antibody_Decay_Rate, and Maternal_Antibodies_Type parameters.	{ "Maternal_Infection_Transmission_Probability": 0.3 }
Max_Individual_Infections	integer	0	1000	1	The limit on the number of infections that an individual can have simultaneously. Enable_Superinfection must be set to 1.	{ "Max_Individual_Infections": 5 }
Population_Density_C50	float	0	3.40E+38	10	The population density at which R0 for a 2.5-arc minute square reaches half of its initial value. Population_Density_Infectivity_Correction must be set to SATURATING_FUNCTION_OF_DENSITY.	{ "Population_Density_C50": 30 }
Population_Density_Infectivity_Correction	enum	NA	NA	CONSTANT_INFECTIVITY	Correction to alter infectivity by population density set in the Population_Density_C50 parameter. Measured in people per square kilometer. Possible values are: CONSTANT_INFECTIVITY SATURATING_FUNCTION_OF_DENSITY Note Sparsely populated areas have a lower infectivity, while densely populated areas have a higher infectivity, which rises to saturate at the Base_Infectivity value.	{ "Population_Density_Infectivity_Correction": "SATURATING_FUNCTION_OF_DENSITY" }
Relative_Sample_Rate_Immune	float	0.001	1	0.1	The relative sampling rate for people who have acquired immunity through recovery or vaccination.	{ "Relative_Sample_Rate_Immune": 0.1 }
Susceptibility_Scaling_Rate	float	0	3.40282e+38	0	The scaling rate for the variation in time of the log-linear susceptibility scaling. Susceptibility_Scaling_Type must be set to LOG_LINEAR_FUNCTION_OF_TIME.	{ "Susceptibility_Scaling_Type": "LOG_LINEAR_FUNCTION_OF_TIME", "Susceptibility_Scaling_Rate": 1.58 }
Susceptibility_Scaling_Type	enum	NA	NA	CONSTANT_SUSCEPTIBILITY	The effect of time on susceptibility. Possible values are: CONSTANT_SUSCEPTIBILITY LOG_LINEAR_FUNCTION_OF_TIME	{ "Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY" }
TB_MDR_Fitness_Multiplier	float	0	1	1	The scale factor for Base_Infectivity to give the infectiousness of MDR-TB strains.	{ "TB_MDR_Fitness_Multiplier": 0.65 }
TB_Smear_Negative_Infectivity_Multiplier	float	0	1	0.25	The scale factor for Base_Infectivity to give the infectiousness of active smear-negative individuals.	{ "TB_Smear_Negative_Infectivity_Multiplier": 1 }
Transmission_Blocking_Immunity_Decay_Rate	float	0	1000	0.001	The rate at which transmission-blocking immunity decays after the base transmission-blocking immunity offset period. Used only when Enable_Immunity and Enable_Immune_Decay parameters are set to true (1).	{ "Transmission_Blocking_Immunity_Decay_Rate": 0.01 }
Transmission_Blocking_Immunity_Duration_Before_Decay	float	0	45000	0	The number of days after infection until transmission-blocking immunity begins to decay. Only used when Enable_Immunity and Enable_Immune_Decay parameters are set to true (1).	{ "Transmission_Blocking_Immunity_Duration_Before_Decay": 90 }
Zoonosis_Rate	float	0	1	0	The daily rate of zoonotic infection per individual. Animal_Reservoir_Type must be set to CONSTANT_ZOONOSIS or ZOONOSIS_FROM_DEMOGRAPHICS. If Animal_Reservoir_Type is set to ZOONOSIS_FROM_DEMOGRAPHICS, the value for the Zoonosis NodeAttribute in the demographics file will override the value set for Zoonosis_Rate.	{ "Zoonosis_Rate": 0.005 }

Input data files ¶

The following parameters set the paths to the the campaign file and the input data files for climate, migration, demographics, and load-balancing.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Air_Migration_Filename	string	NA	NA		The path to the input data file that defines patterns of migration by airplane. Enable_Air_Migration must be set to true (1). The file must be in .bin format.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Air_Migration" : 1, "Air_Migration_Filename": "../Global_1degree_air_migration.bin" }
Air_Temperature_Filename	string	NA	NA	air_temp.json	The path to the input data file that defines air temperature data measured two meters above ground. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format.	{ "Climate_Model": "CLIMATE_BY_DATA", "Air_Temperature_Filename": "Namawala_single_node_air_temperature_daily.bin" }
Campaign_Filename	string	NA	NA		The path to the campaign file. It is required when interventions are part of the simulation and Enable_Interventions is set to true (1). The file must be in .json format.	{ "Enable_Interventions": 1, "Campaign_Filename": "campaign.json" }
Demographics_Filenames	array of strings	NA	NA		An array of the paths to demographics files containing information on the identity and demographics of the region to simulate. The files must be in .json format.	{ "Demographics_Filenames": [ "Namawala_single_node_demographics.json", "Namawala_demographics_overlay.json" ] }
Family_Migration_Filename	string	NA	NA		The name of the binary file to use to configure family migration. Enable_Family_Migration must be set to true (1). The file must be in .bin format.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Family_Migration" 1, "Family_Migration_Filename": "../inputs/family_migration.bin" }
Koppen_Filename	string	NA	NA	koppen_climate.json	The path to the input file used to specify Koppen climate classifications. Climate_Model must be set to CLIMATE_KOPPEN.	{ "Koppen_Filename": "Mad_2_5arcminute_koppen.dat" }
Land_Temperature_Filename	string	NA	NA	land_temp.json	The path of the input file defining temperature data measured at land surface; used only when Climate_Model is set to CLIMATE_BY_DATA. The file must be in .bin format.	{ "Land_Temperature_Filename": "Namawala_single_node_land_temperature_daily.bin" }
Load_Balance_Filename	string	NA	NA	UNINITIALIZED STRING	The path to the input file used when a static load balancing scheme is selected. The file must be in .json format.	{ "Load_Balance_Filename": "GitHub_426_LoadBalance.json" }
Local_Migration_Filename	string	NA	NA		The path of the input file which defines patterns of migration to adjacent nodes by foot travel. The file must be in .bin format.	{ "Local_Migration_Filename": "Local_Migration.bin" }
Rainfall_Filename	string	NA	NA	rainfall.json	The path of the input file which defines rainfall data. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format.	{ "Rainfall_Filename": "Namawala_single_node_rainfall_daily.bin" }
Regional_Migration_Filename	string	NA	NA		The path of the input file which defines patterns of migration by vehicle via road or rail network. If the node is not on a road or rail network, regional migration focuses on the closest hub city in the network. The file must be in .bin format.	{ "Regional_Migration_Filename": "Regional_Migration.bin" }
Relative_Humidity_Filename	string	NA	NA	rel_hum.json	The path of the input file which defines relative humidity data measured 2 meters above ground. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format.	{ "Relative_Humidity_Filename": "Namawala_single_node_relative_humidity_daily.bin" }
Sea_Migration_Filename	string	NA	NA		The path of the input file which defines patterns of migration by ship. Only used when Enable_Sea_Migration is set to true (1). The file must be in .bin format.	{ "Sea_Migration_Filename": "5x5_Households_Work_Migration.bin" }
Serialized_Population_Filenames	array of strings	NA	NA	NA	An array of filenames with serialized population data. The number of filenames must match the number of cores used for the simulation. The files must be in .dtk format. Serialized population files are created using Serialization_Time_Steps.	{ "Serialized_Population_Filenames": [ "state-00010.dtk" ] }
Serialized_Population_Path	string	NA	NA	.	The root path for the serialized population files. Serialized population files are created using Serialization_Time_Steps.	{ "Serialized_Population_Path": "../00_Generic_Version_1_save/output" }

Migration ¶

The following parameters determine aspects of population migration into and outside of a node, including daily commutes, seasonal migration, and one-way moves. Modes of transport includes travel by foot, automobile, sea, or air. Migration can also be configured to move all individuals in a family at the same time.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Air_Migration_Filename	string	NA	NA		The path to the input data file that defines patterns of migration by airplane. Enable_Air_Migration must be set to true (1). The file must be in .bin format.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Air_Migration" : 1, "Air_Migration_Filename": "../Global_1degree_air_migration.bin" }
Air_Migration_Roundtrip_Duration	float	0	10000	1	The average time spent (in days) at the destination node during a round-trip migration by airplane. Migration_Pattern must be set to SINGLE_ROUND_TRIPS.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Air_Migration" : 1, "Migration_Pattern": "SINGLE_ROUND_TRIPS", "Air_Migration_Roundtrip_Duration": 2 }
Air_Migration_Roundtrip_Probability	float	0	1	0.8	The likelihood that an individual who flies to another node will return to the node of origin during the next migration. Enable_Air_Migration must be set to true (1).	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Air_Migration" : 1, "Air_Migration_Roundtrip_Probability": 0.9 }
Enable_Air_Migration	boolean	0	1	0	Controls whether or not migration by air travel will occur. Migration_Model must be set to FIXED_RATE_MIGRATION.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Air_Migration": 1, "Air_Migration_Filename": "../inputs/air_migration.bin" }
Enable_Family_Migration	boolean	0	1	0	Controls whether or not all members of a household can migrate together. All residents must be home before they can leave on the trip. Migration_Model must be set to FIXED_RATE_MIGRATION.	{ "Enable_Migration": "FIXED_RATE_MIGRATION", "Enable_Family_Migration": 1, "Family_Migration_Filename": "../inputs/family_migration.bin" }
Enable_Local_Migration	boolean	0	1	0	Controls whether or not local migration (the diffusion of people in and out of nearby nodes by foot travel) occurs. Migration_Model must be set to FIXED_RATE_MIGRATION.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Local_Migration": 1, "Local_Migration_Filename": "../inputs/local_migration.bin" }
Enable_Migration_Heterogeneity	boolean	0	1	1	Controls whether or not migration rate is heterogeneous among individuals. Set to true (1) to use a migration rate distribution in the demographics file (see NodeAttributes parameters); set to false (0) to use the same migration rate applied to all individuals. Migration_Model must be set to FIXED_RATE_MIGRATION.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Migration_Heterogeneity": 1 }
Enable_Regional_Migration	boolean	0	1	0	Controls whether or not there is migration by road vehicle into and out of nodes in the road network. Migration_Model must be set to FIXED_RATE_MIGRATION.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Regional_Migration": 1, "Regional_Migration_Filename": "../inputs/regional_migration.bin" }
Enable_Sea_Migration	boolean	0	1	0	Controls whether or not there is migration on ships into and out of coastal cities with seaports. Migration_Model must be set to FIXED_RATE_MIGRATION.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Sea_Migration": 1, "Sea_Migration_Filename": "../inputs/sea_migration.bin" }
Family_Migration_Filename	string	NA	NA		The name of the binary file to use to configure family migration. Enable_Family_Migration must be set to true (1). The file must be in .bin format.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Enable_Family_Migration" 1, "Family_Migration_Filename": "../inputs/family_migration.bin" }
Family_Migration_Roundtrip_Duration	float	0	10000	1	The number of days to complete the trip and return to the original node. Migration_Pattern must be set to SINGLE_ROUND_TRIPS.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Migration_Pattern": "SINGLE_ROUND_TRIPS", "Enable_Family_Migration": 1, "Family_Migration_Roundtrip_Duration": 100 }
Local_Migration_Filename	string	NA	NA		The path of the input file which defines patterns of migration to adjacent nodes by foot travel. The file must be in .bin format.	{ "Local_Migration_Filename": "Local_Migration.bin" }
Local_Migration_Roundtrip_Duration	float	0	10000	1	The average time spent (in days) at the destination node during a round-trip migration by foot travel. Migration_Pattern must be set to SINGLE_ROUND_TRIPS.	{ "Enable_Local_Migration": 1, "Migration_Pattern": "SINGLE_ROUND_TRIPS", "Local_Migration_Roundtrip_Duration": 1.0 }
Local_Migration_Roundtrip_Probability	float	0	1	0.95	The likelihood that an individual who walks into a neighboring node will return to the node of origin during the next migration. Only used when Enable_Local_Migration is set to true (1).	{ "Local_Migration_Roundtrip_Probability": 1.0 }
Migration_Model	enum	NA	NA	NO_MIGRATION	Model to use for migration. Possible values are: NO_MIGRATION Migration into and out of nodes will not occur. FIXED_RATE_MIGRATION Migration into and out of nodes will occur at a fixed rate as defined in the migration files. At the beginning of the simulation or whenever an individual has just moved, they pick their next destination and the time and type of the migration. If an individual is on an outbound leg of their journey, they will query the node’s MigrationInfo object and, through probability, pick a new destination; if the individual is inbound, they will travel back to their previous location.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Local_Migration_Filename": "../inputs/local_migration.bin", "Enable_Local_Migration": 1 }
Migration_Pattern	enum	NA	NA	RANDOM_WALK_DIFFUSION	Describes the type of roundtrip used during migration. Migration_Model must be set to FIXED_RATE_MIGRATION. Possible values are: RANDOM_WALK_DIFFUSION Individuals retain no memory of where they came from; every move is to a new destination with no thought of returning home. SINGLE_ROUND_TRIPS There is a certain probability that an individual’s move will be a roundtrip (determined Local_Migration_Roundtrip_Probability, Air_Migration_Roundtrip_Probability, etc.). Otherwise, the departure point is forgotten and the individual does not return to their original location. WAYPOINTS_HOME Individuals go on a multi-step journey along several waypoints and then retrace their steps back along their path once they have reached a maximum number of waypoints from their home.	{ "Migration_Model": "FIXED_RATE_MIGRATION", "Migration_Pattern": "SINGLE_ROUND_TRIPS" }
Regional_Migration_Filename	string	NA	NA		The path of the input file which defines patterns of migration by vehicle via road or rail network. If the node is not on a road or rail network, regional migration focuses on the closest hub city in the network. The file must be in .bin format.	{ "Regional_Migration_Filename": "Regional_Migration.bin" }
Regional_Migration_Roundtrip_Duration	float	0	10000	1	The average time spent (in days) at the destination node during a round-trip migration by road network. Migration_Pattern must be set to SINGLE_ROUND_TRIPS.	{ "Enable_Regional_Migration": 1, "Migration_Pattern": "SINGLE_ROUND_TRIPS", "Regional_Migration_Roundtrip_Duration": 1.0 }
Regional_Migration_Roundtrip_Probability	float	0	1	0.1	The likelihood that an individual who travels by vehicle to another node will return to the node of origin during the next migration. Migration_Pattern must be set to SINGLE_ROUND_TRIPS.	{ "Regional_Migration_Roundtrip_Probability": 1.0 }
Roundtrip_Waypoints	integer	0	1000	10	The maximum number of points reached during a trip before steps are retraced on the return trip home. Migration_Pattern must be set to WAYPOINTS_HOME.	{ "Roundtrip_Waypoints": 5 }
Sea_Migration_Filename	string	NA	NA		The path of the input file which defines patterns of migration by ship. Only used when Enable_Sea_Migration is set to true (1). The file must be in .bin format.	{ "Sea_Migration_Filename": "5x5_Households_Work_Migration.bin" }
Sea_Migration_Roundtrip_Duration	float	0	10000	1	The average time spent at the destination node during a round-trip migration by ship. Used only when Migration_Pattern must be set to SINGLE_ROUND_TRIPS.	{ "Enable_Sea_Migration": 1, "Migration_Pattern": "SINGLE_ROUND_TRIPS", "Sea_Migration_Roundtrip_Duration": 10000 }
Sea_Migration_Roundtrip_Probability	float	0	1	0.25	The likelihood that an individual who travels by ship into a neighboring node will return to the node of origin during the next migration. Used only when Enable_Sea_Migration is set to true (1).	{ "Sea_Migration_Roundtrip_Probability": 0 }
x_Air_Migration	float	0	3.40E+38	1	Scale factor for the rate of migration by air, as provided by the migration file. Enable_Air_Migration must be set to 1.	{ "x_Air_Migration": 1 }
x_Family_Migration	float	0	3.40E+38	1	Scale factor for the rate of migration by families, as provided by the migration file. Enable_Family_Migration must be set to true (1).	{ "x_Family_Migration": 1 }
x_Local_Migration	float	0	3.40E+38	1	Scale factor for rate of migration by foot travel, as provided by the migration file. Enable_Local_Migration must be set to 1.	{ "x_Local_Migration": 1 }
x_Regional_Migration	float	0	3.40E+38	1	Scale factor for the rate of migration by road vehicle, as provided by the migration file. Enable_Regional_Migration must be set to 1.	{ "x_Regional_Migration": 1 }
x_Sea_Migration	float	0	3.40E+38	1	Scale factor for the rate of migration by sea, as provided by the migration file. Enable_Sea_Migration must be set to 1.	{ "x_Sea_Migration": 1 }

Mortality and survival ¶

The following parameter determine mortality and survival characteristics of the disease being modeled and the population in general (non-disease mortality).

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Base_Mortality	float	0	1000	0.001	The base mortality of the infection before accounting for individual immune modification factors. Depending on the setting of Mortality_Time_Course, this is either the daily probability of the disease being fatal (DAILY_MORTALITY) or the probability of death at the end of the infection duration (MORTALITY_AFTER_INFECTIOUS). Enable_Disease_Mortality must be set to 1 (true). This parameter is not used in this simulation type and any values set here will be overridden.	{ "Enable_Vital_Dynamics": 1, "Mortality_Time_Course": "DAILY_MORTALITY", "Base_Mortality": 0.01 }
Death_Rate_Dependence	enum	NA	NA	NOT_INITIALIZED	Determines how likely individuals are to die from natural, non-disease causes. Enable_Natural_Mortality must be set to 1. Possible values are: NOT_INITIALIZED The daily mortality rate is 0, and no one dies from non-disease related causes. NONDISEASE_MORTALITY_BY_AGE_AND_GENDER The individual’s age and gender are taken into account to determine the daily mortality rate. NONDISEASE_MORTALITY_BY_YEAR_AND_AGE_FOR_EACH_GENDER Gender, age, and year, are all taken into account to determine the daily mortality rate. Properties, rates, and bin sizes can be set for non-disease mortality for each gender in the demographics file (see Complex distributions parameters).	{ "Death_Rate_Dependence": "NONDISEASE_MORTALITY_BY_AGE_AND_GENDER" }
Enable_Disease_Mortality	boolean	0	1	1	Controls whether or not individuals are removed from the simulation due to disease deaths. This parameter is not used with this simulation type.	{ "Enable_Disease_Mortality": 1 }
Enable_Natural_Mortality	boolean	0	1	0	Controls whether or not individuals are removed from the simulation due to natural (non-disease) deaths. Enable_Vital_Dynamics must be set to 1 (true).	{ "Enable_Natural_Mortality": 1, "Enable_Vital_Dynamics": 1 }
Mortality_Blocking_Immunity_Decay_Rate	float	0	1000	0.001	The rate at which mortality-blocking immunity decays after the mortality-blocking immunity offset period. Enable_Immune_Decay must be set to 1.	{ "Mortality_Blocking_Immunity_Decay_Rate": 0.1 }
Mortality_Time_Course	enum	NA	NA	DAILY_MORTALITY	The method used to calculate disease deaths. Possible values are: DAILY_MORTALITY Calculated at every time step. MORTALITY_AFTER_INFECTIOUS Calculated once at the end of the disease duration.	{ "Mortality_Time_Course": "MORTALITY_AFTER_INFECTIOUS" }
TB_Active_Mortality_Rate	float	0	1	0.0009589	The mean rate at which active smear-positive individuals die from TB disease. In the absence of treatment, this rate is added to TB_Active_Cure_Rate and TB_Inactivation_Rate to determine the total rate of the active smear-positive state, then the ratio of TB_Active_Mortality_Rate to total rate determines the probability that an active smear-positive individual will die from TB disease after a mean duration of 1 / total rate.	{ "TB_Active_Mortality_Rate": 5e-08 }
TB_Drug_Mortality_Rate	float	0	1	0	The daily rate at which treatment with an anti-TB drug causes death. This parameter, TB_Drug_Inactivation_Rate, TB_Drug_Relapse_Rate, and TB_Drug_Clearance_Rate specify the daily probability of a change in the TB disease state during the course of treatment. The sum of these rates and the total duration of treatment specify the probability that any event occurs. If no event occurs, the individual fails drug treatment.	{ "TB_Drug_Params": { "BurninDrug": { "TB_Drug_Clearance_Rate": 0.0111, "TB_Drug_Inactivation_Rate": 0, "TB_Drug_Mortality_Rate": 0.00162, "TB_Drug_Relapse_Rate": 0.00223, "TB_Drug_Resistance_Rate": 0 } } }
TB_Extrapulmonary_Mortality_Multiplier	float	0	1	0.4	The scale factor for TB_Active_Mortality_Rate to give the mortality rate of active extrapulmonary individuals. In the absence of treatment, the product is added to TB_Active_Cure_Rate and TB_Inactivation_Rate to determine the total rate of the active extrapulmonary state, then the ratio of the aforementioned product to total rate determines the probability that an active extrapulmonary individual will die from TB disease after mean duration of 1 / total rate.	{ "TB_Extrapulmonary_Mortality_Multiplier": 1 }
TB_Smear_Negative_Mortality_Multiplier	float	0	1	0.4	The scale factor to increase the mortality rate set by TB_Active_Mortality_Rate for individuals with smear-negative TB. In the absence of treatment, the product is added to TB_Active_Cure_Rate and TB_Inactivation_Rate to determine the total rate of the active smear-negative state, then the ratio of the aforementioned product to total rate determines the probability that an active smear-negative individual will die from TB disease after mean duration of 1 / total rate.	{ "TB_Smear_Negative_Mortality_Multiplier": 1 }
x_Other_Mortality	float	0	3.40E+38	1	Scale factor for mortality from causes other than the disease being simulated, as provided by the demographics file (see Complex distributions parameters). Enable_Natural_Mortality must be set to 1.	{ "x_Other_Mortality": 1 }

Output settings ¶

The following parameters configure whether or not output reports are created for the simulation, such as reports detailing spatial or demographic data at each time step. By default, the Inset chart output report is always created.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Custom_Reports_Filename

string

NA

UNINITIALIZED STRING

The name of the file containing custom report configuration parameters. Omitting this parameter or setting it to RunAllCustomReports will load all reporters found that are valid for the given simulation type. The file must be in JSON format.

{  
    "Custom_Reports_Filename": "custom_reports.json"
}

Enable_Default_Reporting

boolean

0

1

Controls whether or not the default InsetChart.json report is created.

{
    "Enable_Default_Reporting": 1
}

Enable_Demographics_Reporting

boolean

0

1

Controls whether or not demographic summary data and age-binned reports are outputted to file.

{
    "Enable_Demographics_Reporting": 1
}

Enable_Property_Output

boolean

0

1

0

Controls whether or not to create property output reports, which detail groups as defined in IndividualProperties in the demographics file (see NodeProperties and IndividualProperties parameters). When there is more than one property type, the report will display the channel information for all combinations of the property type groups.

{
    "Enable_Property_Output": 1
}

Enable_Spatial_Output

boolean

0

1

0

Controls whether or not spatial output reports are created. If set to true (1), spatial output reports include all channels listed in the parameter Spatial_Output_Channels.

Note

Spatial output files require significant processing time and disk space.

{
    "Enable_Spatial_Output": 1,
    "Spatial_Output_Channels": [
        "Prevalence",
        "New_Infections"
    ]
}

Report_Event_Recorder

boolean

0

1

0

Set to true (1) to enable or to false (0) to disable the ReportEventRecorder.csv output report.

{
    "Report_Event_Recorder": 1, 
    "Report_Event_Recorder_Events": [
        "VaccinatedA", 
        "VaccineExpiredA", 
        "VaccinatedB", 
        "VaccineExpiredB"
    ], 
    "Report_Event_Recorder_Ignore_Events_In_List": 0 
}			

Report_Event_Recorder_Events

array

NA

The list of events to include or exclude in the ReportEventRecorder.csv output report, based on how Report_Event_Recorder_Ignore_Events_In_List is set.

{
    "Report_Event_Recorder": 1, 
    "Report_Event_Recorder_Events": [
        "VaccinatedA", 
        "VaccineExpiredA", 
        "VaccinatedB", 
        "VaccineExpiredB"
    ], 
    "Report_Event_Recorder_Ignore_Events_In_List": 0 
}				

Report_Event_Recorder_Ignore_Events_In_List

boolean

0

1

0

If set to false (0), only the events listed in the Report_Event_Recorder_Events array will be included in the ReportEventRecorder.csv output report. If set to true (1), only the events listed in the array will be excluded, and all other events will be included. If you want to return all events from the simulation, leave the events array empty.

Value	Events array	Output file
0	No events	No events
0	One or more events	Only the listed events.
1	No events	All events occurring in the simulation.
1	One or more events	All simulation events occurring in the simulation, except for those listed.

{
    "Report_Event_Recorder": 1, 
    "Report_Event_Recorder_Events": [
        "VaccinatedA", 
        "VaccineExpiredA", 
        "VaccinatedB", 
        "VaccineExpiredB"
    ], 
    "Report_Event_Recorder_Ignore_Events_In_List": 0 
}			

Report_Event_Recorder_Individual_Properties

array of strings

NA

[]

Specifies an array of events that will be excluded from the property output report; all events NOT listed in the array will be included in the report. To report all events from the simulation, leave the events array empty.

This example demonstrates reporting all individual property events:

{
    "Report_Event_Recorder_Individual_Properties": []
}	

The following example demonstrates the syntax for excluding particular properties from the report:

{
    "Report_Event_Recorder_Individual_Properties": [
        "Accessibility", 
        "Risk"
    ]
}	

Spatial_Output_Channels

array of strings

NA

[]

An array of channel names for spatial output by node and time step. The data from each channel will be written to a separate binary file. Enable_Spatial_Output must be set to true (1). Possible values are:

Air_Temperature: Data related to air temperature.
Births: Data related to the number of births.
Campaign_Cost: Data related to the costs of a campaign.
Disease_Deaths: Data related to the number of deaths due to disease.
Human_Infectious_Reservoir: Data related to the total infectiousness of the population.
Infection_Rate: Data related to infection rates.
Land_Temperature: Data related to the average land temperature over all nodes.
New_Infections: Data related to the presence of new infections.
New_Reported_Infections: Data related to the presence of reported new infections.
Population: Data related to the total population in the simulation.
Prevalence: Data related to the fraction of the population that is infected.
Rainfall: Data related to the presence of rainfall.
Relative_Humidity: Data related to the presence of relative humidity.

{
    "Spatial_Output_Channels": [
        "Prevalence",
        "New_Infections"
    ]
}        

Population dynamics ¶

The following parameters determine characteristics related to population dynamics, such as age distribution, births, deaths, and gender. The values set here generally interact closely with values in the demographics file.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Age_Initialization_Distribution_Type	enum	NA	NA	DISTRIBUTION_OFF	The method for initializing the age distribution in the simulated population. Possible values are: DISTRIBUTION_OFF All individuals default to age 20 years old. DISTRIBUTION_SIMPLE Individual ages are drawn from a distribution whose functional form is specified in the demographics file using the IndividualAttributes simple Age distribution parameters. DISTRIBUTION_COMPLEX Individual ages are drawn from a piecewise linear function specified in the demographics file complex distribution parameters.	{ "Age_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE" }
Base_Population_Scale_Factor	float	0	3.40E+38	1	The scale factor for InitialPopulation in the demographics file (see NodeAttributes parameters). If Population_Scale_Type is set to FIXED_SCALING, the initial simulation population is uniformly scaled over the entire area to adjust for historical or future population density.	{ "Base_Population_Scale_Factor": 0.0001 }
Birth_Rate_Boxcar_Forcing_Amplitude	float	0	3.40E+38	0	Fractional increase in birth rate during high birth season when Birth_Rate_Time_Dependence is set to ANNUAL_BOXCAR_FUNCTION.	{ "Enable_Vital_Dynamics": 1, "Enable_Birth": 1, "Birth_Rate_Time_Dependence": "ANNUAL_BOXCAR_FUNCTION", "Birth_Rate_Boxcar_Forcing_Amplitude": 0.1 }
Birth_Rate_Boxcar_Forcing_End_Time	float	0	365	0	Day of the year when the high birth rate season ends when Birth_Rate_Time_Dependence is set to ANNUAL_BOXCAR_FUNCTION.	{ "Enable_Vital_Dynamics": 1, "Enable_Birth": 1, "Birth_Rate_Time_Dependence": "ANNUAL_BOXCAR_FUNCTION", "Birth_Rate_Boxcar_Forcing_End_Time": 220 }
Birth_Rate_Boxcar_Forcing_Start_Time	float	0	365	0	Day of the year when the high birth rate season begins when Birth_Rate_Time_Dependence is set to ANNUAL_BOXCAR_FUNCTION.	{ "Enable_Vital_Dynamics": 1, "Enable_Birth": 1, "Birth_Rate_Time_Dependence": "ANNUAL_BOXCAR_FUNCTION", "Birth_Rate_Boxcar_Forcing_Start_Time": 130 }
Birth_Rate_Dependence	enum	NA	NA	FIXED_BIRTH_RATE	The method used to modify the value set in BirthRate in the demographics file (see NodeAttributes parameters). Possible values are: NONE Births are not allowed during the simulation, even if Enable_Birth is set to true (1). FIXED_BIRTH_RATE The absolute rate at which new individuals are born, as set by BirthRate. POPULATION_DEP_RATE Scales the node population to determine the birth rate. If BirthRate is greater than 0.005, a value of 2% per year (0.02/365) is used instead. DEMOGRAPHIC_DEP_RATE Scales the female population within fertility age ranges to determine the birth rate. If BirthRate is greater than 0.005, a value of 1 child every 8 years of fertility [1/8/365(~0.000342)] is used instead. INDIVIDUAL_PREGNANCIES Scales the female population within fertility age ranges to determine the birth rate, but pregnancies are assigned on an individual basis and result in a 40-week pregnancy for a specific individual with a birth at the end. INDIVIDUAL_PREGNANCIES_BY_URBAN_AND_AGE Similar to INDIVIDUAL_PREGNANCIES, but determines the rate based on the FertilityDistribution (in IndividualAttributes) and Urban (in NodeAttributes), as well as the individual’s age. INDIVIDUAL_PREGNANCIES_BY_AGE_AND_YEAR Similar to INDIVIDUAL_PREGNANCIES, but determines the rate based on the FertilityDistribution (in IndividualAttributes), using the individual’s age and the year of the simulation.	{ "Enable_Vital_Dynamics": 1, "Enable_Birth": 1, "Birth_Rate_Dependence": "POPULATION_DEP_RATE" }
Birth_Rate_Sinusoidal_Forcing_Amplitude	float	0	1	0	The amplitude of sinusoidal variations in birth rate when Birth_Rate_Time_Dependence is set to SINUSOIDAL_FUNCTION_OF_TIME.	{ "Enable_Vital_Dynamics": 1, "Enable_Birth": 1, "Birth_Rate_Time_Dependence": "SINUSOIDAL_FUNCTION_OF_TIME", "Birth_Rate_Sinusoidal_Forcing_Amplitude": 0.1 }
Birth_Rate_Sinusoidal_Forcing_Phase	float	0	365	0	The phase of sinusoidal variations in birth rate. Birth_Rate_Time_Dependence must be set to SINUSOIDAL_FUNCTION_OF_TIME.	{ "Birth_Rate_Sinusoidal_Forcing_Phase": 20 }
Birth_Rate_Time_Dependence	enum	NA	NA	NONE	A scale factor that allows the birth rate to be altered by time or season. Enable_Birth must be set to true (1). Possible values are: NONE Birth rate does not vary by time. SINUSOIDAL_FUNCTION_OF_TIME Allows birth rate to be time-dependent, following a sinusoidal shape. Set Birth_Rate_Sinusoidal_Forcing_Amplitude and Birth_Rate_Sinusoidal_Forcing_Phase. ANNUAL_BOXCAR_FUNCTION Allows birth rate to follow a boxcar function, such that it will be equal to zero for an entire time period (e.g. year) except for a single interval in which it is equal to a constant. Set Birth_Rate_Boxcar_Forcing_Amplitude, Birth_Rate_Boxcar_Forcing_End_Time, and Birth_Rate_Boxcar_Forcing_Start_Time.	{ "Enable_Vital_Dynamics": 1, "Enable_Birth": 1, "Birth_Rate_Time_Dependence": "ANNUAL_BOXCAR_FUNCTION" }
Death_Rate_Dependence	enum	NA	NA	NOT_INITIALIZED	Determines how likely individuals are to die from natural, non-disease causes. Enable_Natural_Mortality must be set to 1. Possible values are: NOT_INITIALIZED The daily mortality rate is 0, and no one dies from non-disease related causes. NONDISEASE_MORTALITY_BY_AGE_AND_GENDER The individual’s age and gender are taken into account to determine the daily mortality rate. NONDISEASE_MORTALITY_BY_YEAR_AND_AGE_FOR_EACH_GENDER Gender, age, and year, are all taken into account to determine the daily mortality rate. Properties, rates, and bin sizes can be set for non-disease mortality for each gender in the demographics file (see Complex distributions parameters).	{ "Death_Rate_Dependence": "NONDISEASE_MORTALITY_BY_AGE_AND_GENDER" }
Default_Geography_Initial_Node_Population	integer	0	1000000	1000	When using the built-in demographics for default geography, the initial number of individuals in each node. Note that the built-in demographics feature does not represent a real geographical location and is mostly used for testing. Enable_Demographics_Builtin must be set to true (1).	{ "Enable_Demographics_Builtin": 1, "Default_Geography_Initial_Node_Population": 1000, "Default_Geography_Torus_Size": 3 }
Demographics_Filenames	array of strings	NA	NA		An array of the paths to demographics files containing information on the identity and demographics of the region to simulate. The files must be in .json format.	{ "Demographics_Filenames": [ "Namawala_single_node_demographics.json", "Namawala_demographics_overlay.json" ] }
Enable_Aging	boolean	0	1	1	Controls whether or not individuals in a population age during the simulation. Enable_Vital_Dynamics must be set to true (1).	{ "Enable_Vital_Dynamics": 1, "Enable_Aging": 1 }
Enable_Birth	boolean	0	1	1	Controls whether or not individuals will be added to the simulation by birth. Enable_Vital_Dynamics must be set to true (1). If you want new individuals to have the same intervention coverage as existing individuals, you must add a BirthTriggeredIV to the campaign file.	{ "Enable_Vital_Dynamics": 1, "Enable_Birth": 1 }
Enable_Demographics_Birth	boolean	0	1	0	Controls whether or not newborns have identical or heterogeneous characteristics. Set to false (0) to give all newborns identical characteristics; set to true (1) to allow for heterogeneity in traits such as sickle-cell status. Enable_Birth must be set to true (1).	{ "Enable_Birth": 1, "Enable_Demographics_Birth": 1 }
Enable_Demographics_Builtin	boolean	0	1	0	Controls whether or not built-in demographics for default geography will be used. Note that the built-in demographics feature does not represent a real geographical location and is mostly used for testing. Set to true (1) to define the initial population and number of nodes using Default_Geography_Initial_Node_Population and Default_Geography_Torus_Size. Set to false (0) to use demographics input files defined in Demographics_Filenames.	{ "Enable_Demographics_Builtin": 1, "Default_Geography_Initial_Node_Population": 1000, "Default_Geography_Torus_Size": 3 }
Enable_Demographics_Gender	boolean	0	1	1	Controls whether or not gender ratios are drawn from a Gaussian or 50/50 draw. Set to true (1) to create gender ratios drawn from a male/female ratio that is randomly smeared by a Gaussian of width 1%; set to false (0) to assign a gender ratio based on a 50/50 draw.	{ "Enable_Demographics_Gender": 1 }
Enable_Demographics_Risk	boolean	0	1	0	Controls whether or not the simulation includes the impact of disease risk in demographics. This parameter is not used in this simulation type.	{ "Enable_Demographics_Risk": 1 }
Enable_Vital_Dynamics	boolean	0	1	1	Controls whether or not births and deaths occur in the simulation. Births and deaths must be individually enabled and set.	{ "Enable_Vital_Dynamics":1, "Enable_Birth": 1, "Death_Rate_Dependence": "NONDISEASE_MORTALITY_OFF", "Base_Mortality": 0.002 }
Minimum_Adult_Age_Years	float	0	3.40E+38	15	The age, in years, after which an individual is considered an adult. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP.	{ "Minimum_Adult_Age_Years": 17 }
Population_Density_Infectivity_Correction	enum	NA	NA	CONSTANT_INFECTIVITY	Correction to alter infectivity by population density set in the Population_Density_C50 parameter. Measured in people per square kilometer. Possible values are: CONSTANT_INFECTIVITY SATURATING_FUNCTION_OF_DENSITY Note Sparsely populated areas have a lower infectivity, while densely populated areas have a higher infectivity, which rises to saturate at the Base_Infectivity value.	{ "Population_Density_Infectivity_Correction": "SATURATING_FUNCTION_OF_DENSITY" }
Population_Scale_Type	enum	NA	NA	USE_INPUT_FILE	The method to use for scaling the initial population specified in the demographics input file. Possible values are: USE_INPUT_FILE Turns off population scaling and uses InitialPopulation in the demographics file (see NodeAttributes parameters). FIXED_SCALING Enables Base_Population_Scale_Factor.	{ "Population_Scale_Type": "FIXED_SCALING" }
x_Birth	float	0	3.40E+38	1	Scale factor for birth rate, as provided by the demographics file (see NodeAttributes parameters). Enable_Birth must be set to 1.	{ "x_Birth": 1 }

Sampling ¶

The following parameters determine how a population is sampled in the simulation. While you may want every agent (individual object) to represent a single person, you can often optimize CPU time with without degrading the accuracy of the simulation but having an agent represent multiple people. The sampling rate may be adapted to have a higher or lower sampling rate for particular regions or age groups.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Base_Individual_Sample_Rate	float	0	1	1	The base rate of sampling for individuals, equal to the fraction of individuals in each node being sampled. Reducing the sampling rate will reduce the time needed to run simulations. Individual_Sampling_Type must be set to FIXED_SAMPLING.	{ "Base_Individual_Sample_Rate": 0.01 }
Immune_Threshold_For_Downsampling	float	0	1	0	Threshold on acquisition immunity at which to apply immunity dependent downsampling. Individual_Sampling_Type must set to ADAPTED_SAMPLING_BY_IMMUNE_STATE.	{ "Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_IMMUNE_STATE", "Immune_Threshold_For_Downsampling": 0.5 }
Individual_Sampling_Type	enum	NA	NA	TRACK_ALL	The type of individual human sampling. Possible values are: TRACK_ALL Each person in the population is tracked as a single Individual object with a sampling weight of 1. FIXED_SAMPLING Members of the population are added to the simulation with a probability of Base_Individual_Sample_Rate and sampling weight of 1 / Base_Individual_Sample_Rate. This allows a smaller set of Individual objects to represent the overall population. ADAPTED_SAMPLING_BY_POPULATION_SIZE For each node, a maximum number of Individual objects is tracked (set in Max_Node_Population_Samples). If the population is smaller than this number, each person is tracked with a sampling rate of 1; if the population is larger, members of the population are added to the simulation with a probability of Max_Node_Population_Samples / population and sampling weight of population / Max_Node_Population_Samples. This setting is useful for simulations over large geographic areas with large heterogeneities in population density. ADAPTED_SAMPLING_BY_AGE_GROUP The population is binned by age and each age bin is sampled at a different rate as defined by Sample_Rate parameters. The setting is useful for diseases in which only infants or children are relevant by allowing full sampling of these age groups while using fewer objects to represent the rest of the population. ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE Each node has a maximum number of Individual objects as described in ADAPTED_SAMPLING_BY_POPULATION_SIZE, but when the population grows beyond that threshold, sampling is not done at a fixed rate, but varies by age as described in ADAPTED_SAMPLING_BY_AGE_GROUP. ADAPTED_SAMPLING_BY_IMMUNE_STATE Individuals who have acquired immunity are sampled at a lower rate as defined by Relative_Sample_Rate_Immune and Immune_Threshold_For_Downsampling parameters.	The following example shows how to sampling immune individuals at a lower rate. { "Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_IMMUNE_STATE", "Immune_Threshold_For_Downsampling": 0.5, "Relative_Sample_Rate_Immune": 0.1 } The following example shows how to sampling older individuals at a lower rate. { "Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_AGE_GROUP", "Sample_Rate_0_18mo": 1, "Sample_Rate_10_14": 0.5, "Sample_Rate_15_19": 0.3, "Sample_Rate_18mo_4yr": 1, "Sample_Rate_20_Plus": 0.2, "Sample_Rate_5_9": 1, "Sample_Rate_Birth": 1 }
Max_Node_Population_Samples	float	1	3.40E+38	30	The maximum number of individuals to track in a node. When the population exceeds this number, the sampling rate will drop according to the value set in Individual_Sampling_Type.	{ "Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_POPULATION_SIZE", "Max_Node_Population_Samples": 100000 }
Relative_Sample_Rate_Immune	float	0.001	1	0.1	The relative sampling rate for people who have acquired immunity through recovery or vaccination.	{ "Relative_Sample_Rate_Immune": 0.1 }
Sample_Rate_0_18mo	float	0	1000	1	For age-adapted sampling, the relative sampling rate for infants age 0 to 18 months. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.	{ "Sample_Rate_0_18mo": 1 }
Sample_Rate_10_14	float	0	1000	1	For age-adapted sampling, the relative sampling rate for children age 10 to 14 years. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.	{ "Sample_Rate_10_14": 1 }
Sample_Rate_15_19	float	0	1000	1	For age-adapted sampling, the relative sampling rate for adolescents age 15 to 19 years. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.	{ "Sample_Rate_15_19": 1 }
Sample_Rate_18mo_4yr	float	0	1000	1	For age-adapted sampling, the relative sampling rate for children age 18 months to 4 years. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.	{ "Sample_Rate_18mo_4yr": 1 }
Sample_Rate_20_plus	float	0	1000	1	For age-adapted sampling, the relative sampling rate for adults age 20 and older. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.	{ "Sample_Rate_20_plus": 1 }
Sample_Rate_5_9	float	0	1000	1	For age-adapted sampling, the relative sampling rate for children age 5 to 9 years. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.	{ "Sample_Rate_5_9": 1 }
Sample_Rate_Birth	float	0	1000	1	For age-adapted sampling, the relative sampling rate for births. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.	{ "Sample_Rate_Birth": 1 }

Scalars and multipliers ¶

The following parameters scale or multiply values set in other areas of the configuration file or input data files. This can be especially useful for understanding the sensitivities of disease dynamics to input data without requiring modifications to those base values. For example, one might set x_Birth to a value less than 1 to simulate a lower future birth rate due to increased economic prosperity and available medical technology.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Base_Population_Scale_Factor	float	0	3.40E+38	1	The scale factor for InitialPopulation in the demographics file (see NodeAttributes parameters). If Population_Scale_Type is set to FIXED_SCALING, the initial simulation population is uniformly scaled over the entire area to adjust for historical or future population density.	{ "Base_Population_Scale_Factor": 0.0001 }
Birth_Rate_Time_Dependence	enum	NA	NA	NONE	A scale factor that allows the birth rate to be altered by time or season. Enable_Birth must be set to true (1). Possible values are: NONE Birth rate does not vary by time. SINUSOIDAL_FUNCTION_OF_TIME Allows birth rate to be time-dependent, following a sinusoidal shape. Set Birth_Rate_Sinusoidal_Forcing_Amplitude and Birth_Rate_Sinusoidal_Forcing_Phase. ANNUAL_BOXCAR_FUNCTION Allows birth rate to follow a boxcar function, such that it will be equal to zero for an entire time period (e.g. year) except for a single interval in which it is equal to a constant. Set Birth_Rate_Boxcar_Forcing_Amplitude, Birth_Rate_Boxcar_Forcing_End_Time, and Birth_Rate_Boxcar_Forcing_Start_Time.	{ "Enable_Vital_Dynamics": 1, "Enable_Birth": 1, "Birth_Rate_Time_Dependence": "ANNUAL_BOXCAR_FUNCTION" }
Immunity_Acquisition_Factor	float	0	1000	0	The multiplicative reduction in the probability of reacquiring disease. Enable_Immunity must be set to 1 (true).	{ "Enable_Immunity": 1, "Enable_Immune_Decay": 1, "Immunity_Acquisition_Factor": 0.9 }
Infectivity_Exponential_Baseline	float	0	1	0	The scale factor applied to Base_Infectivity at the beginning of a simulation, before the infectivity begins to grow exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.	{ "Infectivity_Exponential_Baseline": 0.1, "Infectivity_Exponential_Delay": 90, "Infectivity_Exponential_Rate": 45, "Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME" }
Population_Scale_Type	enum	NA	NA	USE_INPUT_FILE	The method to use for scaling the initial population specified in the demographics input file. Possible values are: USE_INPUT_FILE Turns off population scaling and uses InitialPopulation in the demographics file (see NodeAttributes parameters). FIXED_SCALING Enables Base_Population_Scale_Factor.	{ "Population_Scale_Type": "FIXED_SCALING" }
Susceptibility_Scaling_Rate	float	0	3.40282e+38	0	The scaling rate for the variation in time of the log-linear susceptibility scaling. Susceptibility_Scaling_Type must be set to LOG_LINEAR_FUNCTION_OF_TIME.	{ "Susceptibility_Scaling_Type": "LOG_LINEAR_FUNCTION_OF_TIME", "Susceptibility_Scaling_Rate": 1.58 }
Susceptibility_Scaling_Type	enum	NA	NA	CONSTANT_SUSCEPTIBILITY	The effect of time on susceptibility. Possible values are: CONSTANT_SUSCEPTIBILITY LOG_LINEAR_FUNCTION_OF_TIME	{ "Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY" }
TB_Active_Presymptomatic_Infectivity_Multiplier	float	0	1	0.0274	The scale factor for Base_Infectivity to give the infectiousness of active presymptomatic individuals.	{ "TB_Active_Presymptomatic_Infectivity_Multiplier": 0.19999999999999998 }
TB_Drug_Efficacy_Multiplier_Failed	float	0	1	1	The scale factor for drug efficacy parameters (TB_Drug_Inactivation_Rate and TB_Drug_Clearance_Rate) to give the efficacy of first-line drugs when given to individuals who have failed treatment.	{ "TB_Drug_Efficacy_Multiplier_Failed": 1.0 }
TB_Drug_Efficacy_Multiplier_MDR	float	0	1	1	The scale factor for drug efficacy parameters (TB_Drug_Inactivation_Rate and TB_Drug_Clearance_Rate) to give the efficacy of first-line drugs when given to individuals with MDR-TB.	{ "TB_Drug_Efficacy_Multiplier_MDR": 1.0 }
TB_Drug_Efficacy_Multiplier_Relapsed	float	0	1	1	The scale factor for drug efficacy parameters (TB_Drug_Inactivation_Rate and TB_Drug_Clearance_Rate) to give the efficacy of first-line drugs when given to individuals who have relapsed after treatment.	{ "TB_Drug_Efficacy_Multiplier_Relapsed": 1.0 }
TB_Extrapulmonary_Mortality_Multiplier	float	0	1	0.4	The scale factor for TB_Active_Mortality_Rate to give the mortality rate of active extrapulmonary individuals. In the absence of treatment, the product is added to TB_Active_Cure_Rate and TB_Inactivation_Rate to determine the total rate of the active extrapulmonary state, then the ratio of the aforementioned product to total rate determines the probability that an active extrapulmonary individual will die from TB disease after mean duration of 1 / total rate.	{ "TB_Extrapulmonary_Mortality_Multiplier": 1 }
TB_MDR_Fitness_Multiplier	float	0	1	1	The scale factor for Base_Infectivity to give the infectiousness of MDR-TB strains.	{ "TB_MDR_Fitness_Multiplier": 0.65 }
TB_Smear_Negative_Infectivity_Multiplier	float	0	1	0.25	The scale factor for Base_Infectivity to give the infectiousness of active smear-negative individuals.	{ "TB_Smear_Negative_Infectivity_Multiplier": 1 }
TB_Smear_Negative_Mortality_Multiplier	float	0	1	0.4	The scale factor to increase the mortality rate set by TB_Active_Mortality_Rate for individuals with smear-negative TB. In the absence of treatment, the product is added to TB_Active_Cure_Rate and TB_Inactivation_Rate to determine the total rate of the active smear-negative state, then the ratio of the aforementioned product to total rate determines the probability that an active smear-negative individual will die from TB disease after mean duration of 1 / total rate.	{ "TB_Smear_Negative_Mortality_Multiplier": 1 }
x_Air_Migration	float	0	3.40E+38	1	Scale factor for the rate of migration by air, as provided by the migration file. Enable_Air_Migration must be set to 1.	{ "x_Air_Migration": 1 }
x_Birth	float	0	3.40E+38	1	Scale factor for birth rate, as provided by the demographics file (see NodeAttributes parameters). Enable_Birth must be set to 1.	{ "x_Birth": 1 }
x_Family_Migration	float	0	3.40E+38	1	Scale factor for the rate of migration by families, as provided by the migration file. Enable_Family_Migration must be set to true (1).	{ "x_Family_Migration": 1 }
x_Local_Migration	float	0	3.40E+38	1	Scale factor for rate of migration by foot travel, as provided by the migration file. Enable_Local_Migration must be set to 1.	{ "x_Local_Migration": 1 }
x_Other_Mortality	float	0	3.40E+38	1	Scale factor for mortality from causes other than the disease being simulated, as provided by the demographics file (see Complex distributions parameters). Enable_Natural_Mortality must be set to 1.	{ "x_Other_Mortality": 1 }
x_Regional_Migration	float	0	3.40E+38	1	Scale factor for the rate of migration by road vehicle, as provided by the migration file. Enable_Regional_Migration must be set to 1.	{ "x_Regional_Migration": 1 }
x_Sea_Migration	float	0	3.40E+38	1	Scale factor for the rate of migration by sea, as provided by the migration file. Enable_Sea_Migration must be set to 1.	{ "x_Sea_Migration": 1 }

Simulation setup ¶

These parameters determine the basic setup of a simulation including the type of simulation you are running, such as “GENERIC_SIM” or “MALARIA_SIM”, the simulation duration, and the time step duration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Base_Year	float	1900	2200	2015	The absolute time in years when the simulation begins. This can be combined with CampaignEventByYear to trigger campaign events. This parameter is not used in this simulation type.	{ "Base_Year": 1960 }
Config_Name	string	NA	NA	UNINITIALIZED STRING	The optional, user-supplied title naming a configuration file.	{ "Config_Name": "My_First_Config" }
Enable_Interventions	boolean	0	1	0	Controls whether or not campaign interventions will be used in the simulation. Set Campaign_Filename to the path of the file that contains the campaign interventions.	{ "Enable_Interventions": 1, "Campaign_Filename": "campaign.json" }
Enable_Skipping	boolean	0	1	0	Controls whether or not the simulation uses an optimization that can increase performance by up to 50% in some cases by probablistically exposing individuals rather than exposing every single person. Useful in low-prevalence, high-population scenarios. This parameter is not used in this simulation type.	{ "Exposure_Skipping": 1 }
Listed_Events	array of strings	NA	NA	[]	The list of valid, user-defined events that will be included in the campaign. Any event used in the campaign must either be one of the built-in events or in this list.	{ "Listed_Events": [ "Vaccinated", "VaccineExpired" ] }
Memory_Usage_Halting_Threshold_Working_Set_MB	integer	0	1.00E+06	8000	The maximum size (in MB) of working set memory before the system throws an exception and halts.	{ "Memory_Usage_Halting_Threshold_Working_Set_MB": 4000 }
Memory_Usage_Warning_Threshold_Working_Set_MB	integer	0	1.00E+06	7000	The maximum size (in MB) of working set memory before memory usage statistics are written to the log regardless of log level.	{ "Memory_Usage_Warning_Threshold_Working_Set_MB": 3500 }
Node_Grid_Size	float	0.00416	90	0.004167	The spatial resolution indicating the node grid size for a simulation in degrees.	{ "Node_Grid_Size": 0.042 }
Run_Number	integer	0	2147480000	1	Sets the random number seed through a bit manipulation process. When running a multi-core simulation, combines with processor rank to produce independent random number streams for each process.	{ "Run_Number": 1 }
Serialization_Time_Steps	array of integers	0	2.15E+09		The list of time steps after which to save the serialized state to file. 0 (zero) indicates the initial state before simulation, n indicates after the nth time step. By default, no serialized state is saved. The serialized population files can then be loaded at the beginning of a simulation using Serialized_Population_Filenames and Serialized_Population_Path.	{ "Serialization_Time_Steps": [ 0, 10 ] }
Serialized_Population_Filenames	array of strings	NA	NA	NA	An array of filenames with serialized population data. The number of filenames must match the number of cores used for the simulation. The files must be in .dtk format. Serialized population files are created using Serialization_Time_Steps.	{ "Serialized_Population_Filenames": [ "state-00010.dtk" ] }
Serialized_Population_Path	string	NA	NA	.	The root path for the serialized population files. Serialized population files are created using Serialization_Time_Steps.	{ "Serialized_Population_Path": "../00_Generic_Version_1_save/output" }
Simulation_Duration	float	0	1000000	1	The elapsed time (in days) from the start to the end of a simulation.	{ "Simulation_Duration": 7300 }
Simulation_Timestep	float	0	1000000	1	The duration of a simulation time step, in days.	{ "Simulation_Timestep": 1 }
Simulation_Type	enum	NA	NA	GENERIC_SIM	The type of disease being simulated. Possible IDM-supported values are: GENERIC_SIM VECTOR_SIM MALARIA_SIM TB_SIM STI_SIM HIV_SIM	{ "Simulation_Type": "STI_SIM" }
Start_Time	float	0	1000000	1	The time, in days, when the simulation begins. This time is used to identify the starting values of the temporal input data, such as specifying which day’s climate values should be used for the first day of the simulation. Note The Start_Day of campaign events is in absolute time, so time relative to the beginning of the simulation depends on this parameter.	{ "Start_Time": 365 }

Symptoms and diagnosis ¶

The following parameters determine the characteristics of tuberculosis diagnosis and symptoms.

Parameter	Data type	Maximum	Default	Description	Example
TB_Extrapulmonary_Fraction_Adult	float	1	0	The fraction of active cases in adults (age 15 or over) that are extrapulmonary. The sum of this and TB_Smear_Positive_Fraction_Adult must be less than or equal to 1.	{ "TB_Extrapulmonary_Fraction_Adult": 0.33 }
TB_Extrapulmonary_Fraction_Child	float	1	0	The fraction of active cases in children (under age 15) that are extrapulmonary. The sum of this and TB_Smear_Positive_Fraction_Child must be less than or equal to 1.	{ "TB_Extrapulmonary_Fraction_Child": 0.33 }
TB_Smear_Negative_Infectivity_Multiplier	float	1	0.25	The scale factor for Base_Infectivity to give the infectiousness of active smear-negative individuals.	{ "TB_Smear_Negative_Infectivity_Multiplier": 1 }
TB_Smear_Negative_Mortality_Multiplier	float	1	0.4	The scale factor to increase the mortality rate set by TB_Active_Mortality_Rate for individuals with smear-negative TB. In the absence of treatment, the product is added to TB_Active_Cure_Rate and TB_Inactivation_Rate to determine the total rate of the active smear-negative state, then the ratio of the aforementioned product to total rate determines the probability that an active smear-negative individual will die from TB disease after mean duration of 1 / total rate.	{ "TB_Smear_Negative_Mortality_Multiplier": 1 }
TB_Smear_Positive_Fraction_Adult	float	1	1	The fraction of active cases in adults (age 15 and over) that are smear-positive. The sum of TB_Smear_Positive_Fraction_Adult and TB_Extrapulmonary_Fraction_Adult must be less than or equal to 1. This sum plus the fraction of smear-negative adults is equal to 1.	{ "TB_Smear_Positive_Fraction_Adult": 1 }
TB_Smear_Positive_Fraction_Child	float	1	1	The fraction of active cases in children (under age 15) that are smear-positive. The sum of TB_Smear_Positive_Fraction_Child and TB_Extrapulmonary_Fraction_Child must be less than or equal to 1. This sum plus the fraction of smear-negative children is equal to 1.	{ "TB_Smear_Positive_Fraction_Child": 1 }

Campaign parameters¶

The parameters described in this reference section can be added to the JavaScript Object Notation (JSON) formatted campaign file to determine the interventions that are used to control the spread of disease and where, when, how, and to whom the interventions are distributed. Additionally, the campaign file specifies the details of the outbreak of the disease itself.

In the configuration file, you must set the Enable_Interventions parameter to 1 and set the Campaign_Filename parameter to the name of the campaign file for the interventions to take place. The campaign file must be in the same directory as the configuration file.

The campaign file is hierarchically organized into logical groups of parameters that can have arbitrary levels of nesting. It contains an Events array of campaign events, each of which contains a JSON object describing the event coordinator, which in turn contains a nested JSON object describing the intervention. At the same level as the Events array is the boolean Use_Defaults to indicate whether or not to use the default values for required parameters that are not included in the file. It is common to include JSON keys for campaign name, event name, or intervention name; these are informational only and not used by EMOD. For more information on how to set up the elements in a campaign file, see Create a campaign file.

The skeletal JSON example below illustrates the basic file structure (this does not include all required parameters for each object). Note that the nested JSON elements have been organized to best illustrate their hierarchy, but that many files in the Regression directory of the EMOD GitHub repository list the parameters and nested objects differently.

{
    "Campaign_Name": "Campaign example",
    "Use_Defaults": 1,
    "Events": [{
        "Event_Name": "The first event",
        "class": "CampaignEvent",
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "Intervention_Name": "The vaccine",
                "class": "SimpleVaccine"
            }
        }
    }, {
        "Event_Name": "The second event",
        "class": "CampaignEvent",
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "Intervention_Name": "The disease outbreak",
                "class": "OutbreakIndividual"
            }
        }
    }]
}

Warning

The event containing the outbreak “intervention” must be the last one listed in the campaign file or none of the disease control interventions will take place.

The topics below contain only parameters available when using the tuberculosis simulation type.

Events and event coordinators¶

Campaign events determine when and where an intervention is distributed during a campaign. Event coordinators are nested within the campaign event JSON object and determine who receives the intervention.

The following events and event coordinators are available to use with the tuberculosis simulation type.

CampaignEvent¶

The CampaignEvent event class determines when to distribute the intervention based on the first day of the simulation. See the following JSON example and table, which shows all available parameters for this campaign event.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Event_Coordinator_Config

JSON object

NA

An object that specifies how the event is handled by the simulation. It specifies which Event Coordinator class will handle the event, and then configures the coordinator. This description starts with specifying class, and then the other fields depend on the class.

{
    "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Demographic_Coverage": 1.0
    }
}

Nodeset_Config

JSON object

NA

An object that specifies in which nodes the interventions will be distributed. For example, if running a single location simulation, or an event that is active in all nodes, then specify class NodeSetAll within the Nodeset_Config brackets.

Possible classes are:

NodeSetAll: Distribute to all nodes in the simulation.
NodeSetNodeList: Distribute to the nodes in the comma-separated list of node IDs specified by the Node_List parameter.
NodeSetPolygon: Distribute to the nodes that fall within a polygon specified by the comma-separated list of polygon points in the Vertices parameter (set Polygon_Format to SHAPE).

{
    "Nodeset_Config": {
        "class": "NodeSetNodeList",
        "Node_List": [1, 5, 27]
    }
}

{
    "Nodeset_Config": {
        "class": "NodeSetPolygon",
        "Polygon_Format": "SHAPE",
        "Vertices": "-122.320726936496,47.6597902588541 -122.320406460197,47.6520988276782 -122.308388598985,47.6471314450438"
    }
}

Start_Day

float

0

3.40E+3

1

The day of the simulation to activate the event’s event coordinator.

{
    "Start_Day": 0,
    "End_Day": 100
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Event_Name": "Individual outbreak",
        "Start_Day": 1,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 1.0,
            "Intervention_Config": {
                "class": "OutbreakIndividual"
            }
        }
    }]
}

CalendarEventCoordinator¶

The CalendarEventCoordinator coordinator class distributes individual-level interventions at a specified time and coverage. See the following JSON example and table, which shows all available parameters for this event coordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Demographic_Coverage	float	0	1	1	The fraction of individuals in the target demographic that will receive this intervention.	{ "Demographic_Coverage": 1 }
Distribution_Coverages	array of floats	0	1	0	A vector of floats for the fraction of individuals that will receive this intervention in a CalendarEventCoordinator.	{ "Distribution_Times": [100, 200, 400, 800, 1200], "Distribution_Coverages": [0.01, 0.05, 0.1, 0.2, 1.0] }
Distribution_Times	array of integers	1	2.15E+0	0	A vector of integers for simulation times at which distribution of events occurs in a CalendarEventCoordinator.	{ "Distribution_Times": [100, 200, 400, 800, 1200], "Distribution_Coverages": [0.01, 0.05, 0.1, 0.2, 1.0] }
Intervention_Config	JSON object	NA	NA	NA	The nested JSON of the actual intervention to be distributed by this event coordinator.	{ "Intervention_Config": { "class": "OutbreakIndividual", "Incubation_Period_Override": 1, "Outbreak_Source": "PrevalenceIncrease" } }
Node_Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. You can specify AND and OR combinations of key:value pairs with this parameter.	The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk. { "Node_Property_Restrictions": [{ "Place": "URBAN", "Risk": "MED" }, { "Place": "RURAL", "Risk": "LOW" } ] }
Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.	{ "Property_Restrictions": [ "Risk:HIGH" ] }
Property_Restrictions_Within_Node	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.	The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk. { "Property_Restrictions_Within_Node": [ {"Risk": "HIGH", "Geographic": "URBAN"}, {"Risk": "MEDIUM", "Geographic": "URBAN"} ] }
Target_Age_Max	float	0	3.40E+3	3.40E+38	The upper end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Age_Min	float	0	3.40E+3	0	The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Demographic	enum	NA	NA	Everyone	The target demographic group. Possible values are: Everyone ExplicitAgeRanges ExplicitAgeRangesAndGender ExplicitGender PossibleMothers ArrivingAirTravellers DepartingAirTravellers ArrivingRoadTravellers DepartingRoadTravellers AllArrivingTravellers AllDepartingTravellers ExplicitDiseaseState	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Gender	enum	NA	NA	All	Specifies the gender restriction for the intervention. Possible values are: Male Female All	{ "Target_Gender": "Male" }
Target_Residents_Only	boolean	NA	NA	0	When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).	{ "Target_Residents_Only": 1 }
Timesteps_Between_Repetitions	integer	-1	10000	-1	The repetition interval.	{ "Timesteps_Between_Repetitions": 50 }

{
    "Events": [{
        "class": "CampaignEvent",
        "Event_Name": "High-risk vaccination",
        "Start_Day": 1,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "CalendarEventCoordinator",
            "Demographic_Coverage": 1,
            "Property_Restrictions": [
                "Risk:High"
            ],
            "Number_Repetitions": 1,
            "Timesteps_Between_Repetitions": 0,
            "Target_Demographic": "Everyone",
            "Target_Residents_Only": 1,
            "Distribution_Times": [100, 200, 400, 800, 1200],
            "Distribution_Coverages": [0.01, 0.05, 0.1, 0.2, 1.0],
            "Intervention_Config": {
                "Cost_To_Consumer": 0,
                "Vaccine_Take": 1,
                "Vaccine_Type": "AcquisitionBlocking",
                "class": "SimpleVaccine",
                "Waning_Config": {
                    "Initial_Effect": 1,
                    "Box_Duration": 1825,
                    "class": "WaningEffectBox"
                }
            }
        }
    }]
}

CommunityHealthWorkerEventCoordinator¶

The CommunityHealthWorkerEventCoordinator coordinator class is used to model a health care worker’s ability to distribute interventions to the nodes and individual in their coverage area. This coordinator distributes a limited number of interventions per day, and can create a backlog of individuals or nodes requiring the intervention. For example, individual-level interventions could be distribution of drugs and node-level interventions could be spraying houses with insecticide.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Amount_In_Shipment	integer	1	2.15E+0	2.15E+09	The number of interventions (such as vaccine doses) that a health worker or clinic receives in a shipment. Interventions can only be distributed if they are in stock; stock is updated every Days_Between_Shipments with the Amount_In_Shipment.	{ "Amount_In_Shipment": 10 }
Days_Between_Shipments	float	1	3.40E+3	3.40E+38	The number of days to wait before a clinic or health worker receives a new shipment of interventions (such as vaccine doses). Interventions can only be distributed if they are in stock; stock is updated every Days_Between_Shipments with the Amount_In_Shipment.	{ "Days_Between_Shipments": 30 }
Demographic_Coverage	float	0	1	1	The fraction of individuals in the target demographic that will receive this intervention.	{ "Demographic_Coverage": 1 }
Duration	float	0	3.40E+3	3.40E+38	The number of days for an event coordinator to be active before it expires.	{ "Duration": 65 }
Initial_Amount	float	0	3.40E+3	6	The initial amount of stock of interventions (such as vaccine doses). Interventions can only be distributed if they are in stock; stock is updated every Days_Between_Shipments with the Amount_In_Shipment.	{ "Initial_Amount": 10 }
Initial_Amount_Distribution_Type	enum	NA	NA	NOT_INITIALIZED	The distribution type to set when initializing Initial_Amount. Possible values are: NOT_INITIALIZED No distribution set. FIXED_DURATION A constant duration. UNIFORM_DURATION A uniform random draw for the duration. GAUSSIAN_DURATION Duration of the active period is defined by the mean and standard deviation of the Gaussian distribution. Negative values are truncated at zero. EXPONENTIAL_DURATION The active period is the mean of the exponential random draw. POISSON_DURATION The active period is the mean of the random Poisson draw. LOG_NORMAL_DURATION The active period is a log normal distribution defined by a mean and log width. BIMODAL_DURATION The distribution is bimodal, the duration is a fraction of the active period for a specified period of time and equal to the active period otherwise. PIECEWISE_CONSTANT The distribution is specified with a list of years and a matching list of values. The duration at a given year is that specified for the nearest previous year. PIECEWISE_LINEAR The distribution is specified with a list of years and matching list of values. The duration at a given year is a linear interpolation of the specified values. WEIBULL_DURATION The duration is a Weibull distribution with a given scale and shape. DUAL_TIMESCALE_DURATION The duration is two exponential distributions with given means.	{ "Initial_Amount_Distribution_Type": "FIXED_DURATION" }
Initial_Amount_Max	float	0	3.40E+3	0	The maximum amount of initial stock when Initial_Amount_Distribution_Type is set to UNIFORM_DISTRIBUTION.	{ "Initial_Amount_Distribution_Type": "UNIFORM_DURATION", "Initial_Amount_Min": 5, "Initial_Amount_Max": 10 }
Initial_Amount_Mean	float	0	3.40E+3	6	The average amount of initial stock when Initial_Amount_Distribution_Type is set to GAUSSIAN_DISTRIBUTION.	{ "Initial_Amount_Distribution_Type": "GAUSSIAN_DISTRIBUTION", "Initial_Amount_Std_Dev": 1, "Initial_Amount_Mean": 5 }
Initial_Amount_Min	float	0	3.40E+3	0	The minimum amount of initial stock when Initial_Amount_Distribution_Type is set to UNIFORM_DISTRIBUTION.	{ "Initial_Amount_Distribution_Type": "UNIFORM_DURATION", "Initial_Amount_Min": 5, "Initial_Amount_Max": 10 }
Initial_Amount_Std_Dev	float	0	3.40E+3	1	The standard deviation for the amount of initial stock when Initial_Amount_Distribution_Type is set to GAUSSIAN_DISTRIBUTION.	{ "Initial_Amount_Distribution_Type": "GAUSSIAN_DISTRIBUTION", "Initial_Amount_Std_Dev": 1, "Initial_Amount_Mean": 5 }
Intervention_Config	JSON object	NA	NA	NA	The nested JSON of the actual intervention to be distributed by this event coordinator.	{ "Intervention_Config": { "class": "BroadcastEvent", "Broadcast_Event": "EventFromIntervention" } }
Max_Distributed_Per_Day	integer	1	2.15E+0	2.15E+09	The maximum number of interventions (such as vaccine doses) that can be distributed by health workers or clinics in a given day.	{ "Max_Distributed_Per_Day": 1 }
Max_Stock	integer	0	2.15E+0	2.15E+09	The maximum number of interventions (such as vaccine doses) that can be stored by a health worker or clinic. If the amount of interventions in a new shipment and current stock exceeds this value, only the number of interventions specified by this value will be stored.	{ "Max_Stock": 12 }
Node_Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. You can specify AND and OR combinations of key:value pairs with this parameter.	The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk. { "Node_Property_Restrictions": [{ "Place": "URBAN", "Risk": "MED" }, { "Place": "RURAL", "Risk": "LOW" } ] }
Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.	{ "Property_Restrictions": [ "Risk:HIGH" ] }
Property_Restrictions_Within_Node	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.	The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk. { "Property_Restrictions_Within_Node": [ {"Risk": "HIGH", "Geographic": "URBAN"}, {"Risk": "MEDIUM", "Geographic": "URBAN"} ] }
Target_Age_Min	float	0	3.40E+3	0	The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Demographic	enum	NA	NA	Everyone	The target demographic group. Possible values are: Everyone ExplicitAgeRanges ExplicitAgeRangesAndGender ExplicitGender PossibleMothers ArrivingAirTravellers DepartingAirTravellers ArrivingRoadTravellers DepartingRoadTravellers AllArrivingTravellers AllDepartingTravellers ExplicitDiseaseState	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Gender	enum	NA	NA	All	Specifies the gender restriction for the intervention. Possible values are: Male Female All	{ "Target_Gender": "Male" }
Target_Residents_Only	boolean	NA	NA	0	When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).	{ "Target_Residents_Only": 1 }
Trigger_Condition_List	array of strings	NA	NA	NoTrigger	The list of events that are of interest to the community health worker (CHW). If one of these events occurs, the individual or node is put into a queue to receive the CHW’s intervention. The CHW processes the queue when the event coordinator is updated. See Event list for possible values.	{ "Trigger_Condition_List": ["ListenForEvent"] }
Waiting_Period	float	0	3.40E+3	3.40E+38	The number of days a person or node can be in the queue waiting to get the intervention from the community health worker (CHW). If a person or node is in the queue, they will not be re-added to the queue if the event that would add them to the queue occurs. This allows them to maintain their priority, however they could be removed from the queue due to this waiting period.	{ "Waiting_Period": 15 }

{
    "Events": [{
        "class": "CampaignEvent",
        "Start_Day": 1,
        "Nodeset_Config": {
            "class": "NodeSetNodeList",
            "Node_List": [2, 3, 4]
        },
        "Event_Coordinator_Config": {
            "class": "CommunityHealthWorkerEventCoordinator",
            "Duration": 400,
            "Distribution_Rate": 25,
            "Waiting_Period": 10,
            "Days_Between_Shipments": 25,
            "Amount_In_Shipment": 250,
            "Max_Stock": 1000,
            "Initial_Amount_Distribution_Type": "FIXED_DURATION",
            "Initial_Amount": 500,
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 1.0,
            "Property_Restrictions": [],
            "Target_Residents_Only": 0,
            "Trigger_Condition_List": ["NewInfectionEvent"],
            "Intervention_Config": {
                "class": "SimpleVaccine",
                "Cost_To_Consumer": 10.0,
                "Vaccine_Take": 1,
                "Vaccine_Type": "AcquisitionBlocking",
                "Waning_Config": {
                    "class": "WaningEffectBox",
                    "Initial_Effect": 1,
                    "Box_Duration": 200
                }
            }
        }
    }]
}

CoverageByNodeEventCoordinator¶

The CoverageByNodeEventCoordinator coordinator class distributes individual-level interventions and is similar to the StandardInterventionDistributionEventCoordinator, but adds the ability to specify different demographic coverages by node. If no coverage has been specified for a particular node ID, the coverage will be zero. See the following JSON example and table, which shows all available parameters for this event coordinator.

Note

This can only be used with individual-level interventions, but EMOD will not produce an error if you attempt to use it with an node-level intervention.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Coverage_By_Node	array of arrays	NA	NA	NA	An array of (nodeID, coverage) pairs configuring the demographic coverage of interventions by node for the targeted populations. The coverage value must be a float between 0 and 1.	{ "Event_Coordinator_Config": { "Coverage_By_Node": [ [ 1, 0.6 ], [ 2, 0.9 ], [ 3, 0.1 ] ] } }
Demographic_Coverage	float	0	1	1	The fraction of individuals in the target demographic that will receive this intervention.	{ "Demographic_Coverage": 1 }
Intervention_Config	JSON object	NA	NA	NA	The nested JSON of the actual intervention to be distributed by this event coordinator.	{ "Intervention_Config": { "class": "OutbreakIndividual", "Incubation_Period_Override": 1, "Outbreak_Source": "PrevalenceIncrease" } }
Node_Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. You can specify AND and OR combinations of key:value pairs with this parameter.	The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk. { "Node_Property_Restrictions": [{ "Place": "URBAN", "Risk": "MED" }, { "Place": "RURAL", "Risk": "LOW" } ] }
Number_Repetitions	integer	-1	1000	1	The number of times an intervention is given, used with Timesteps_Between_Repetitions.	{ "Event_Coordinator_Config": { "Intervention_Config": { "class": "Outbreak", "Num_Cases": 1 }, "Number_Repetitions": 10, "Timesteps_Between_Repetitions": 50, "class": "StandardInterventionDistributionEventCoordinator" } }
Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.	{ "Property_Restrictions": [ "Risk:HIGH" ] }
Property_Restrictions_Within_Node	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.	The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk. { "Property_Restrictions_Within_Node": [ {"Risk": "HIGH", "Geographic": "URBAN"}, {"Risk": "MEDIUM", "Geographic": "URBAN"} ] }
Target_Age_Min	float	0	3.40E+3	0	The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Demographic	enum	NA	NA	Everyone	The target demographic group. Possible values are: Everyone ExplicitAgeRanges ExplicitAgeRangesAndGender ExplicitGender PossibleMothers ArrivingAirTravellers DepartingAirTravellers ArrivingRoadTravellers DepartingRoadTravellers AllArrivingTravellers AllDepartingTravellers ExplicitDiseaseState	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Gender	enum	NA	NA	All	Specifies the gender restriction for the intervention. Possible values are: Male Female All	{ "Target_Gender": "Male" }
Target_Residents_Only	boolean	NA	NA	0	When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).	{ "Target_Residents_Only": 1 }
Timesteps_Between_Repetitions	integer	-1	10000	-1	The repetition interval.	{ "Timesteps_Between_Repetitions": 50 }

{
    "Events": [{
        "class": "CampaignEvent",
        "Start_Day": 0,
        "Nodeset_Config": {
            "Node_List": [
                1,
                2,
                3
            ],
            "class": "NodeSetNodeList"
        },
        "Event_Coordinator_Config": {
            "class": "CoverageByNodeEventCoordinator",
            "Target_Demographic": "Everyone",
            "Coverage_By_Node": [
                [1, 0.6],
                [2, 0.9],
                [3, 0.1]
            ],
            "Intervention_Config": {
                "Cost_To_Consumer": 10.0,
                "Reduced_Transmit": 0,
                "Vaccine_Take": 1,
                "Vaccine_Type": "AcquisitionBlocking",
                "Waning_Config": {
                    "Box_Duration": 3650,
                    "Initial_Effect": 1,
                    "class": "WaningEffectBox"
                },
                "class": "SimpleVaccine"
            }
        }
    }]
}

GroupInterventionDistributionEventCoordinator¶

The GroupInterventionDistributionEventCoordinator coordinator class is similar to StandardInterventionDistributionEventCoordinator, but allows individuals to be targeted by disease state (the Target_Demographic parameter must be set to ExplicitDiseaseState). Disease state cannot be combined with age or gender targeting. See the following JSON example and table, which shows all available parameters for this event coordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Demographic_Coverage	float	0	1	1	The fraction of individuals in the target demographic that will receive this intervention.	{ "Demographic_Coverage": 1 }
Demographic_Coverage	float	0	1	1	The fraction of individuals in the target demographic that will receive this intervention.	{ "Demographic_Coverage": 1 }
Intervention_Config	JSON object	NA	NA	NA	The nested JSON of the actual intervention to be distributed by this event coordinator.	{ "Intervention_Config": { "class": "OutbreakIndividual", "Incubation_Period_Override": 1, "Outbreak_Source": "PrevalenceIncrease" } }
Node_Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. You can specify AND and OR combinations of key:value pairs with this parameter.	The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk. { "Node_Property_Restrictions": [{ "Place": "URBAN", "Risk": "MED" }, { "Place": "RURAL", "Risk": "LOW" } ] }
Number_Repetitions	integer	-1	1000	1	The number of times an intervention is given, used with Timesteps_Between_Repetitions.	{ "Event_Coordinator_Config": { "Intervention_Config": { "class": "Outbreak", "Num_Cases": 1 }, "Number_Repetitions": 10, "Timesteps_Between_Repetitions": 50, "class": "StandardInterventionDistributionEventCoordinator" } }
Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.	{ "Property_Restrictions": [ "Risk:HIGH" ] }
Property_Restrictions_Within_Node	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.	The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk. { "Property_Restrictions_Within_Node": [ {"Risk": "HIGH", "Geographic": "URBAN"}, {"Risk": "MEDIUM", "Geographic": "URBAN"} ] }
Property_Restrictions_Within_Node	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.	The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk. { "Property_Restrictions_Within_Node": [ {"Risk": "HIGH", "Geographic": "URBAN"}, {"Risk": "MEDIUM", "Geographic": "URBAN"} ] }
Target_Age_Min	float	0	3.40E+3	0	The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Demographic	enum	NA	NA	Everyone	The target demographic group. Possible values are: Everyone ExplicitAgeRanges ExplicitAgeRangesAndGender ExplicitGender PossibleMothers ArrivingAirTravellers DepartingAirTravellers ArrivingRoadTravellers DepartingRoadTravellers AllArrivingTravellers AllDepartingTravellers ExplicitDiseaseState	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Disease_State	enum	NA	NA	Everyone	The disease state group targeted by this intervention. Possible values are: Everyone Infected ActiveInfection LatentInfection MDR TreatmentNaive HasFailedTreatment HIVNegative AcitveHadTreatment	{ "Target_Disease_State": "HIV_Negative" }
Target_Gender	enum	NA	NA	All	Specifies the gender restriction for the intervention. Possible values are: Male Female All	{ "Target_Gender": "Male" }
Target_Residents_Only	boolean	NA	NA	0	When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).	{ "Target_Residents_Only": 1 }
Target_Residents_Only	boolean	NA	NA	0	When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).	{ "Target_Residents_Only": 1 }
Timesteps_Between_Repetitions	integer	-1	10000	-1	The repetition interval.	{ "Timesteps_Between_Repetitions": 50 }

{
    "Use_Defaults": 1,
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 50,
        "Event_Coordinator_Config": {
            "class": "GroupInterventionDistributionEventCoordinator",
            "Target_Demographic": "ExplicitDiseaseState",
            "Number_Repetitions": 1,
            "Property_Restrictions": [],
            "Target_Disease_State": "HasFailedTreatment",
            "Demographic_Coverage": 1,
            "Intervention_Config": {
                "class": "AntiTBDrug",
                "Cost_To_Consumer": 1,
                "Drug_Type": "FirstLineCombo",
                "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT",
                "Primary_Decay_Time_Constant": 10,
                "Remaining_Doses": 1,
                "TB_Drug_Clearance_Rate": 0.1,
                "TB_Drug_Inactivation_Rate": 0.00000001,
                "TB_Drug_Resistance_Rate": 0.000000001,
                "TB_Drug_Relapse_Rate": 0.0000001,
                "Reduced_Transmit": 1
            }
        }
    }]
}

MultiInterventionEventCoordinator¶

The MultiInterventionEventCoordinator coordinator class distributes multiple individual-level or node- level interventions at the same time to the same set of covered individuals or nodes. You cannot specify a mix of individual-level and node-level interventions within the same event coordinator. This enables correlated distributions, such as dosing of multiple drugs with different regimens. See the following JSON example and table, which shows all available parameters for this event coordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Demographic_Coverage	float	0	1	1	The fraction of individuals in the target demographic that will receive this intervention.	{ "Demographic_Coverage": 1 }
Intervention_Configs	array of JSON objects	NA	NA	NA	The array of nested JSON parameters for the interventions to be distributed by this event coordinator.	{ "Intervention_Configs": [{ "Cost_To_Consumer": 1, "Dosing_Type": "FullTreatmentCourse", "Drug_Type": "DHA", "class": "AntimalarialDrug" }, { "Cost_To_Consumer": 1, "Dosing_Type": "FullTreatmentCourse", "Drug_Type": "Piperaquine", "class": "AntimalarialDrug" } ] }
Node_Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. You can specify AND and OR combinations of key:value pairs with this parameter.	The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk. { "Node_Property_Restrictions": [{ "Place": "URBAN", "Risk": "MED" }, { "Place": "RURAL", "Risk": "LOW" } ] }
Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.	{ "Property_Restrictions": [ "Risk:HIGH" ] }
Property_Restrictions_Within_Node	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.	The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk. { "Property_Restrictions_Within_Node": [ {"Risk": "HIGH", "Geographic": "URBAN"}, {"Risk": "MEDIUM", "Geographic": "URBAN"} ] }
Target_Age_Min	float	0	3.40E+3	0	The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Demographic	enum	NA	NA	Everyone	The target demographic group. Possible values are: Everyone ExplicitAgeRanges ExplicitAgeRangesAndGender ExplicitGender PossibleMothers ArrivingAirTravellers DepartingAirTravellers ArrivingRoadTravellers DepartingRoadTravellers AllArrivingTravellers AllDepartingTravellers ExplicitDiseaseState	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Gender	enum	NA	NA	All	Specifies the gender restriction for the intervention. Possible values are: Male Female All	{ "Target_Gender": "Male" }
Target_Residents_Only	boolean	NA	NA	0	When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).	{ "Target_Residents_Only": 1 }

{
    "Events": [{
        "class": "CampaignEvent",
        "Start_Day": 10,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "MultiInterventionEventCoordinator",
            "Intervention_Configs": [{
                    "class": "PropertyValueChanger",
                    "Target_Property_Key": "Risk",
                    "Target_Property_Value": "HIGH",
                    "Daily_Probability": 1.0,
                    "Maximum_Duration": 0,
                    "Revert": 0
                },
                {
                    "class": "BroadcastEvent",
                    "Broadcast_Event": "PVC_Distributed"
                }
            ]
        }
    }]
}

NChooserEventCoordinator¶

The NChooserEventCoordinator coordinator class is used to distribute an individual-level intervention to exactly N people of a targeted demographic. This contrasts with other event coordinators that distribute an intervention to a percentage of the population, not to an exact count. See the following JSON example and table, which shows all available parameters for this event coordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Age_Ranges_Years	array of JSON objects	NA	NA	NA	A list of age ranges that individuals must be in to qualify for an intervention. Each age range is a JSON object with a minimum and a maximum property. An individual is considered in range if their age is greater than or equal to the minimum age and less than the maximum age, in floating point years of age. It must have the same number of objects as Num_Targeted_XXX has elements.	{ "Age_Ranges_Years": [{ "Min": 10, "Max": 19 }, { "Min": 30, "Max": 39 }, { "Min": 50, "Max": 59 }], "Num_Targeted_Males": [600000, 400000, 200000], "Num_Targeted_Females": [500000, 300000, 100000] }
Distributions	array of JSON objects	NA	NA	NA	The ordered list of elements defining when, to whom, and how many interventions to distribute.	{ "Distributions": { "Start_Day": 10, "End_Day": 20, "Property_Restrictions_Within_Node": [], "Age_Ranges_Years": [{ "Min": 10, "Max": 19 }, { "Min": 40, "Max": 49 }], "Num_Targeted": [100, 300] } }
End_Day	float	0	3.40E+3	3.40E+38	The day to stop distributing the intervention. No interventions are distributed on this day or going forward.	{ "Start_Day": 10, "End_Day": 20 }
Intervention_Config	JSON object	NA	NA	NA	The nested JSON of the actual intervention to be distributed by this event coordinator.	{ "Intervention_Config": { "class": "OutbreakIndividual", "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease", "Incubation_Period_Override": 1 } }
Max	float	0	125	125	This parameter determines the maximum age, in years for individuals to be included in the targeted population. An individual is considered in range if their age is greater than or equal to the minimum age and less than the maximum age, in floating point years of age.	{ "Age_Ranges_Years": [{ "Min": 20, "Max": 29 }, { "Min": 50, "Max": 59 }] }
Min	float	0	125	0	This parameter determines the minimum age, in years for individuals to be included in the targeted population. An individual is considered in range if their age is greater than or equal to the minimum age and less than the maximum age, in floating point years of age.	{ "Age_Ranges_Years": [{ "Min": 20, "Max": 29 }, { "Min": 50, "Max": 59 }] }
Num_Targeted	array of integers	0	2.15E+0	0	The number of individuals to target with the intervention. Note that this value will be scaled up by the population scaling factor equal to Base_Population_Scale_Factor. If using this parameter, Num_Targeted_Males and Num_Targeted_Females must be empty.	{ "Age_Ranges_Years": [{ "Min": 10, "Max": 19 }, { "Min": 40, "Max": 49 }], "Num_Targeted": [100, 300] }
Num_Targeted_Females	array of integers	0	2.15E+0	0	The number of female individuals to distribute interventions to during this time period. If using this parameter with Num_Targeted_Males to target specific genders, they both must be the same length, and Num_Targeted must be empty.	{ "Age_Ranges_Years": [{ "Min": 10, "Max": 19 }, { "Min": 30, "Max": 39 }, { "Min": 50, "Max": 59 }], "Num_Targeted_Males": [600000, 400000, 200000], "Num_Targeted_Females": [500000, 300000, 100000] }
Num_Targeted_Males	array of integers	0	2.15E+0	0	The number of male individuals to distribute interventions to during this time period. If using this parameter with Num_Targeted_Females to target specific genders, they both must be the same length, and Num_Targeted must be empty.	{ "Age_Ranges_Years": [{ "Min": 10, "Max": 19 }, { "Min": 30, "Max": 39 }, { "Min": 50, "Max": 59 }], "Num_Targeted_Males": [600000, 400000, 200000], "Num_Targeted_Females": [500000, 300000, 100000] }
Property_Restrictions_Within_Node	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.	The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk. { "Property_Restrictions_Within_Node": [ {"Risk": "HIGH", "Geographic": "URBAN"}, {"Risk": "MEDIUM", "Geographic": "URBAN"} ] }
Start_Day	float	0	3.40E+3	0	The day to start distributing the intervention.	{ "Start_Day": 0, "End_Day": 100 }

{
    "Use_Defaults": 1,
    "Events": [{
        "class": "CampaignEvent",
        "Start_Day": 1,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config__KP1": "",
        "Event_Coordinator_Config": {
            "class": "NChooserEventCoordinator",
            "Distributions": [{
                "Start_Day": 10,
                "End_Day": 11,
                "Property_Restrictions_Within_Node": [{
                    "QualityOfCare": "Bad"
                }],
                "Age_Ranges_Years": [{
                    "Min": 20,
                    "Max": 40
                }],
                "Num_Targeted": [
                    99999999
                ]
            }],
            "Intervention_Config": {
                "class": "ControlledVaccine",
                "Cost_To_Consumer": 10,
                "Vaccine_Type": "AcquisitionBlocking",
                "Vaccine_Take": 1.0,
                "Waning_Config": {
                    "class": "WaningEffectMapLinear",
                    "Initial_Effect": 1.0,
                    "Expire_At_Durability_Map_End": 1,
                    "Durability_Map": {
                        "Times": [
                            0,
                            50,
                            100
                        ],
                        "Values": [
                            1.0,
                            1.0,
                            0.0
                        ]
                    }
                },
                "Distributed_Event_Trigger": "Vaccinated",
                "Expired_Event_Trigger": "VaccineExpired",
                "Duration_To_Wait_Before_Revaccination": 0
            }
        }
    }]
}

SimpleDistributionEventCoordinator¶

The SimpleInterventionDistributionEventCoordinator coordinator class distributes individual- level or node-level interventions and is a simplified version of the StandardInterventionDistributionEventCoordinator. There is reduced complexity, such as there is no targeting restrictions on demographics, properties, age, gender, or other target values. See the following JSON example and table, which shows all available parameters for this event coordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Coverage	float	0	1	1	The fraction of individuals that will receive this intervention.	{ "Coverage": 1.0 }
Intervention_Config	JSON object	NA	NA	NA	The nested JSON of the actual intervention to be distributed by this event coordinator.	{ "Intervention_Config": { "class": "OutbreakIndividual", "Incubation_Period_Override": 1, "Outbreak_Source": "PrevalenceIncrease" } }

{
    "Events": [{
        "class": "CampaignEvent",
        "Event_Name": "Male circumcision for initial population",
        "Start_Day": 1,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "SimpleInterventionDistributionEventCoordinator",
            "Coverage": 0.5,
            "Intervention_Config": {
                "class": "MaleCircumcision",
                "Circumcision_Reduced_Acquire": 0.6
            }
        }
    }]
}

StandardInterventionDistributionEventCoordinator¶

The StandardInterventionDistributionEventCoordinator coordinator class distributes an individual-level or node-level intervention to a specified fraction of individuals or nodes within a node set. Recurring campaigns can be created by specifying the number of times distributions should occur and the time between repetitions. See the following JSON example and table, which shows all available parameters for this event coordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Demographic_Coverage	float	0	1	1	The fraction of individuals in the target demographic that will receive this intervention.	{ "Demographic_Coverage": 1 }
Intervention_Config	JSON object	NA	NA	NA	The nested JSON of the actual intervention to be distributed by this event coordinator.	{ "Intervention_Config": { "class": "OutbreakIndividual", "Incubation_Period_Override": 1, "Outbreak_Source": "PrevalenceIncrease" } }
Node_Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. You can specify AND and OR combinations of key:value pairs with this parameter.	The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk. { "Node_Property_Restrictions": [{ "Place": "URBAN", "Risk": "MED" }, { "Place": "RURAL", "Risk": "LOW" } ] }
Number_Repetitions	integer	-1	1000	1	The number of times an intervention is given, used with Timesteps_Between_Repetitions.	{ "Event_Coordinator_Config": { "Intervention_Config": { "class": "Outbreak", "Num_Cases": 1 }, "Number_Repetitions": 10, "Timesteps_Between_Repetitions": 50, "class": "StandardInterventionDistributionEventCoordinator" } }
Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.	{ "Property_Restrictions": [ "Risk:HIGH" ] }
Property_Restrictions_Within_Node	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.	The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk. { "Property_Restrictions_Within_Node": [ {"Risk": "HIGH", "Geographic": "URBAN"}, {"Risk": "MEDIUM", "Geographic": "URBAN"} ] }
Target_Age_Max	float	0	3.40E+3	3.40E+38	The upper end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Age_Min	float	0	3.40E+3	0	The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Demographic	enum	NA	NA	Everyone	The target demographic group. Possible values are: Everyone ExplicitAgeRanges ExplicitAgeRangesAndGender ExplicitGender PossibleMothers ArrivingAirTravellers DepartingAirTravellers ArrivingRoadTravellers DepartingRoadTravellers AllArrivingTravellers AllDepartingTravellers ExplicitDiseaseState	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Gender	enum	NA	NA	All	Specifies the gender restriction for the intervention. Possible values are: Male Female All	{ "Target_Gender": "Male" }
Target_Residents_Only	boolean	NA	NA	0	When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).	{ "Target_Residents_Only": 1 }
Timesteps_Between_Repetitions	integer	-1	10000	-1	The repetition interval.	{ "Timesteps_Between_Repetitions": 50 }

{
    "Use_Defaults": 1,
    "Events": [{
        "Event_Name": "Outbreak",
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 1,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Demographic_Coverage": 0.005,
            "Intervention_Config": {
                "Outbreak_Source": "PrevalenceIncrease",
                "class": "OutbreakIndividual"
            }
        }
    }]
}

Node-level interventions¶

Node-level interventions determine what will be distributed to nodes to reduce the spread of a disease. For example, spraying larvicide in a village to kill mosquito larvae is a node-level malaria intervention. Sometimes this can be an intermediate intervention that schedules another intervention. Node-level disease outbreaks are also configured as “interventions”.

The following node-level interventions are available for use with the tuberculosis simulation type.

BirthTriggeredIV¶

The BirthTriggeredIV intervention class monitors for birth events and then distributes an actual intervention to the new individuals as specified in Actual_IndividualIntervention_Config.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Actual_IndividualIntervention_Config	JSON object	NA	NA	NA	The configuration of an actual individual intervention sought. Selects a class for the intervention and configures the parameters specific for that intervention class.	{ "Actual_IndividualIntervention_Config": { "class": "OutbreakIndividual", "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease" } }
Demographic_Coverage	float	0	1	1	The fraction of individuals in the target demographic that will receive this intervention.	{ "Demographic_Coverage": 1 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Duration	float	-1	3.40E+3	-1	The number of days to continue this intervention. Note For BirthTriggeredIV, specifying a value of -1 results in indefinite persistence of the birth-triggered intervention.	{ "Duration": -1 }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.	{ "Property_Restrictions": [ "Risk:HIGH" ] }
Property_Restrictions_Within_Node	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.	The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk. { "Property_Restrictions_Within_Node": [ {"Risk": "HIGH", "Geographic": "URBAN"}, {"Risk": "MEDIUM", "Geographic": "URBAN"} ] }
Target_Age_Max	float	0	3.40E+3	3.40E+38	The upper end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Demographic	enum	NA	NA	Everyone	The target demographic group. Possible values are: Everyone ExplicitAgeRanges ExplicitAgeRangesAndGender ExplicitGender PossibleMothers ArrivingAirTravellers DepartingAirTravellers ArrivingRoadTravellers DepartingRoadTravellers AllArrivingTravellers AllDepartingTravellers ExplicitDiseaseState	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Gender	enum	NA	NA	All	Specifies the gender restriction for the intervention. Possible values are: Male Female All	{ "Target_Gender": "Male" }
Target_Residents_Only	boolean	NA	NA	0	When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).	{ "Target_Residents_Only": 1 }

{
    "Use_Defaults": 1,
    "Events": [{
        "class": "CampaignEventByYear",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Year": 1960,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "class": "BirthTriggeredIV",
                "Target_Demographic": "ExplicitGender",
                "Target_Gender": "Male",
                "Demographic_Coverage": 1,
                "Actual_IndividualIntervention_Config": {
                    "class": "HIVSigmoidByYearAndSexDiagnostic",
                    "New_Property_Value": "InterventionStatus:None",
                    "Ramp_Min": 0.0,
                    "Ramp_Max": 0.3,
                    "Ramp_MidYear": 2002.0,
                    "Ramp_Rate": 0.5,
                    "Positive_Diagnosis_Event": "HIVNeedsMaleCircumcision"
                }
            }
        }

    }]
}

ImportPressure¶

The ImportPressure intervention class extends the ImportCases outbreak event. Rather than importing a deterministic number of cases on a scheduled day, ImportPressure applies a set of per-day rates of importation of infected individuals, over a corresponding set of durations. ImportPressure inherits from Outbreak; the Antigen and Genome parameters are defined as they are for all Outbreak events.

Warning

Be careful when configuring import pressure in multi-node simulations. Daily_Import_Pressures defines a rate of per-day importation for each node that the intervention is distributed to. In a 10 node simulation with Daily_Import_Pressures = [0.1, 5.0], the total importation rate summed over all nodes will be 1/day and 50/day during the two time periods. You must divide the per-day importation rates by the number of nodes.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Antigen	integer	0	10	0	The antigenic ID of the outbreak infection.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease", "class": "OutbreakIndividual" } }
Daily_Import_Pressures	array of floats	0	3.40E+3	0	The rate of per-day importation for each node that the intervention is distributed to.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Durations": [100, 100, 100, 100, 100, 100, 100], "Daily_Import_Pressures": [0.1, 5.0, 0.2, 1.0, 2.0, 0.0, 10.0], "class": "ImportPressure" } }
Durations	array of integers	0	2.15E+0	1	The durations over which to apply import pressure.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Durations": [100, 100, 100, 100, 100, 100, 100], "Daily_Import_Pressures": [0.1, 5.0, 0.2, 1.0, 2.0, 0.0, 10.0], "class": "ImportPressure" } }
Genome	integer	-2.15E+0	2.15E+0	0	The genetic ID of the outbreak infection.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease", "class": "OutbreakIndividual", "Incubation_Period_Override": 0 } }
Import_Age	float	0	43800	365	The age (in days) of infected import cases.	{ "Import_Age": 10000 }
Incubation_Period_Override	integer	-1	2.15E+0	-1	The incubation period, in days, that infected individuals will go through before becoming infectious. This value overrides the incubation period set in the configuration file. Set to -1 to honor the configuration parameter settings.	{ "Incubation_Period_Override": 0 }
Number_Cases_Per_Node	integer	0	2.15E+0	1	The number of new cases of Outbreak to import (per node). Note This will increase the population and there is no control over demographics of these individuals.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Outbreak_Source": "ImportCases", "Number_Cases_Per_Node": 10, "class": "Outbreak" } }

{
    "Use_Defaults": 1,
    "Campaign_Name": "Initial Seeding",
    "Events": [{
        "class": "CampaignEvent",
        "Start_Day": 1,
        "Event_Name": "Outbreak",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 1.0,
            "Intervention_Config": {
                "Antigen": 0,
                "Genome": 0,
                "Durations": [100, 100, 100, 100, 100, 100, 100],
                "Daily_Import_Pressures": [0.1, 5.0, 0.2, 1.0, 2.0, 0.0, 10.0],
                "class": "ImportPressure"
            }
        }
    }]
}

MigrateFamily¶

The MigrateFamily intervention class tells residents of the targeted node to go on a family trip. The duration of time residents wait before migration can be configured; the “timer” for this duration does not start until all residents are “home”.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Duration_At_Node_Distribution_Type	enum	NA	NA	NOT_INITIALIZED	The shape of the distribution for the amount of time spent at the destination node for intervention-based migration. Possible values are: NOT_INITIALIZED No distribution set. FIXED_DURATION A constant duration. UNIFORM_DURATION A uniform random draw for the duration. GAUSSIAN_DURATION Duration of the active period is defined by the mean and standard deviation of the Gaussian distribution. Negative values are truncated at zero. EXPONENTIAL_DURATION The active period is the mean of the exponential random draw. POISSON_DURATION The active period is the mean of the random Poisson draw. LOG_NORMAL_DURATION The active period is a log normal distribution defined by a mean and log width. BIMODAL_DURATION The distribution is bimodal, the duration is a fraction of the active period for a specified period of time and equal to the active period otherwise. PIECEWISE_CONSTANT The distribution is specified with a list of years and a matching list of values. The duration at a given year is that specified for the nearest previous year. PIECEWISE_LINEAR The distribution is specified with a list of years and matching list of values. The duration at a given year is a linear interpolation of the specified values. WEIBULL_DURATION The duration is a Weibull distribution with a given scale and shape. DUAL_TIMESCALE_DURATION The duration is two exponential distributions with given means.	{ "Duration_At_Node_Distribution_Type": "GAUSSIAN_DURATION" }
Duration_At_Node_Exponential_Period	float	0	3.40E+3	6	The period (1/rate) to use for an exponential distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to EXPONENTIAL_DURATION.	{ "Duration_At_Node_Distribution_Type": "EXPONENTIAL_DURATION", "Duration_At_Node_Exponential_Period": 4 }
Duration_At_Node_Fixed	float	0	3.40E+3	6	The value to use for a fixed distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to FIXED_DURATION.	{ "Duration_At_Node_Distribution_Type" : "FIXED_DURATION", "Duration_At_Node_Fixed": 10 }
Duration_At_Node_Gausian_Mean	float	0	3.40E+3	6	The mean value to use for a Gaussian distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to GAUSSIAN_DURATION.	{ "Duration_At_Node_Distribution_Type": "GAUSSIAN_DURATION", "Duration_At_Node_Gausian_Mean": 9.0, "Duration_At_Node_Gausian_StdDev": 2.0 }
Duration_At_Node_Gausian_StdDev	float	0	3.40E+3	1	The standard deviation to use for a Gaussian distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to GAUSSIAN_DURATION.	{ "Duration_At_Node_Distribution_Type": "GAUSSIAN_DURATION", "Duration_At_Node_Gausian_Mean": 9.0, "Duration_At_Node_Gausian_StdDev": 2.0 }
Duration_At_Node_Poisson_Mean	float	0	3.40E+3	6	The mean to use for a Poisson distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to POISSON_DURATION.	{ "Duration_At_Node_Distribution_Type": "POISSON_DURATION", "Duration_At_Node_Poisson_Mean": 4 }
Duration_At_Node_Uniform_Max	float	0	3.40E+3	0	The maximum value to use for a uniform distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to UNIFORM_DURATION.	{ "Duration_At_Node_Distribution_Type": "UNIFORM_DURATION", "Duration_At_Node_Uniform_Max": 75, "Duration_At_Node_Uniform_Min": 45 }
Duration_At_Node_Uniform_Min	float	0	3.40E+3	0	The minimum value to use for a uniform distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to UNIFORM_DURATION.	{ "Duration_At_Node_Distribution_Type": "UNIFORM_DURATION", "Duration_At_Node_Uniform_Max": 75, "Duration_At_Node_Uniform_Min": 45 }
Duration_Before_Leaving_Distribution_Type	enum	NA	NA	NOT_INITIALIZED	The shape of the distribution of the number of days to wait before migrating to the destination node for intervention-based migration. Possible values are: NOT_INITIALIZED No distribution set. FIXED_DURATION A constant duration. UNIFORM_DURATION A uniform random draw for the duration. GAUSSIAN_DURATION Duration of the active period is defined by the mean and standard deviation of the Gaussian distribution. Negative values are truncated at zero. EXPONENTIAL_DURATION The active period is the mean of the exponential random draw. POISSON_DURATION The active period is the mean of the random Poisson draw. LOG_NORMAL_DURATION The active period is a log normal distribution defined by a mean and log width. BIMODAL_DURATION The distribution is bimodal, the duration is a fraction of the active period for a specified period of time and equal to the active period otherwise. PIECEWISE_CONSTANT The distribution is specified with a list of years and a matching list of values. The duration at a given year is that specified for the nearest previous year. PIECEWISE_LINEAR The distribution is specified with a list of years and matching list of values. The duration at a given year is a linear interpolation of the specified values. WEIBULL_DURATION The duration is a Weibull distribution with a given scale and shape. DUAL_TIMESCALE_DURATION The duration is two exponential distributions with given means.	{ "Duration_Before_Leaving_Distribution_Type": "FIXED_DURATION" }
Duration_Before_Leaving_Exponential_Period	float	0	3.40E+3	6	The period (1/rate) to use for an exponential distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to EXPONENTIAL_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "EXPONENTIAL_DURATION", "Duration_Before_Leaving_Exponential_Period": 4 }
Duration_Before_Leaving_Fixed	float	0	3.40E+3	6	The value to use for a fixed distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to FIXED_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "FIXED_DURATION", "Duration_Before_Leaving_Fixed": 4 }
Duration_Before_Leaving_Gausian_Mean	float	0	3.40E+3	6	The mean value to use for a Gaussian distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to GAUSSIAN_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "GAUSSIAN_DURATION", "Duration_Before_Leaving_Gausian_Mean": 9.0, "Duration_Before_Leaving_Gausian_StdDev": 2.0 }
Duration_Before_Leaving_Gausian_StdDev	float	0	3.40E+3	1	The standard deviation to use for a Gaussian distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to GAUSSIAN_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "GAUSSIAN_DURATION", "Duration_Before_Leaving_Gausian_Mean": 9.0, "Duration_Before_Leaving_Gausian_StdDev": 2.0 }
Duration_Before_Leaving_Poisson_Mean	float	0	3.40E+3	6	The mean to use for a Poisson distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to POISSON_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "POISSON_DURATION", "Duration_Before_Leaving_Poisson_Mean": 3.0, }
Duration_Before_Leaving_Uniform_Max	float	0	3.40E+3	0	The maximum value to use for a uniform distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to UNIFORM_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "UNIFORM_DURATION", "Duration_Before_Leaving_Uniform_Max": 14, "Duration_Before_Leaving_Uniform_Min": 0, }
Duration_Before_Leaving_Uniform_Min	float	0	3.40E+3	0	The minimum value to use for a uniform distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to UNIFORM_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "UNIFORM_DURATION", "Duration_Before_Leaving_Uniform_Max": 14, "Duration_Before_Leaving_Uniform_Min": 0, }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
Is_Moving	boolean	NA	NA	0	Set to true (1) to indicate the individual is permanently moving to a new home node for intervention-based migration.	{ "Is_Moving": 1 }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
NodeID_To_Migrate_To	integer	0	2.15E+0	0	The destination node ID for intervention-based migration.	{ "NodeID_To_Migrate_To": 26 }

{
    "Use_Defaults": 1,
    "Events": [{
        "class": "CampaignEvent",
        "Start_Day": 1,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "class": "NodeLevelHealthTriggeredIV",
                "Trigger_Condition_List": ["NewInfectionEvent"],
                "Demographic_Coverage": 1.0,
                "Actual_NodeIntervention_Config": {
                    "class": "MigrateFamily",
                    "NodeID_To_Migrate_To": 4,
                    "Duration_Before_Leaving_Distribution_Type": "FIXED_DURATION",
                    "Duration_Before_Leaving_Fixed": 0,
                    "Duration_At_Node_Distribution_Type": "FIXED_DURATION",
                    "Duration_At_Node_Fixed": 10,
                    "Is_Moving": 0
                }
            }
        }
    }]
}

NodeLevelHealthTriggeredIV¶

The NodeLevelHealthTriggeredIV intervention class distributes an intervention to an individual when a specific event occurs. NodeLevelHealthTriggeredIV monitors for event triggers from individuals, and when found, will distribute the intervention. For example, NodeLevelHealthTriggeredIV can be configured such that all individuals will be given a diagnostic intervention when they transition from susceptible to infectious. During the simulation, when individuals become infected, they broadcast the NewInfectionEvent trigger and NodeLevelHealthTriggeredIV distributes the diagnostic intervention to them.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Actual_IndividualIntervention_Config	JSON object	NA	NA	NA	The configuration of an actual individual intervention sought. Selects a class for the intervention and configures the parameters specific for that intervention class.	{ "Actual_IndividualIntervention_Config": { "class": "OutbreakIndividual", "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease" } }
Actual_NodeIntervention_Config	JSON object	NA	NA	NA	The configuration of the actual node-level intervention sought. This parameter selects a class for the intervention and configures the parameters specific for that intervention class.	{ "Actual_NodeIntervention_Config": { "class": "OutbreakIndividual", "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease" } }
Blackout_Event_Trigger	enum	NA	NA	NoTrigger	The event to broadcast if an intervention cannot be distributed due to the Blackout_Period. See Event list for possible values.	{ "Blackout_Event_Trigger" : "Blackout" }
Blackout_On_First_Occurrence	boolean	NA	NA	0	If set to true (1), individuals will enter the blackout period after the first occurrence of an event in the Trigger_Condition_List.	{ "Blackout_On_First_Occurrence": 0 }
Blackout_Period	float	0	3.40E+3	0	After the initial intervention distribution, the time, in days, to wait before distributing the intervention again. If it cannot distribute due to the blackout period, it will broadcast the user-defined Blackout_Event_Trigger.	{ "Blackout_Period" : 0.0 }
Demographic_Coverage	float	0	1	1	The fraction of individuals in the target demographic that will receive this intervention.	{ "Demographic_Coverage": 1 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Distribute_On_Return_Home	boolean	NA	NA	0	When set to true (1), individuals will receive an intervention upon returning home if that intervention was originally distributed while the individual was away.	{ "Distribute_On_Return_Home" : 1 }
Duration	float	-1	3.40E+3	-1	The number of days to continue this intervention. Note For BirthTriggeredIV, specifying a value of -1 results in indefinite persistence of the birth-triggered intervention.	{ "Duration": -1 }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Node_Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information. You can specify AND and OR combinations of key:value pairs with this parameter.	The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk. { "Node_Property_Restrictions": [{ "Place": "URBAN", "Risk": "MED" }, { "Place": "RURAL", "Risk": "LOW" } ] }
Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.	{ "Property_Restrictions": [ "Risk:HIGH" ] }
Property_Restrictions_Within_Node	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.	The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk. { "Property_Restrictions_Within_Node": [ {"Risk": "HIGH", "Geographic": "URBAN"}, {"Risk": "MEDIUM", "Geographic": "URBAN"} ] }
Target_Age_Max	float	0	3.40E+3	3.40E+38	The upper end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Demographic	enum	NA	NA	Everyone	The target demographic group. Possible values are: Everyone ExplicitAgeRanges ExplicitAgeRangesAndGender ExplicitGender PossibleMothers ArrivingAirTravellers DepartingAirTravellers ArrivingRoadTravellers DepartingRoadTravellers AllArrivingTravellers AllDepartingTravellers ExplicitDiseaseState	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Gender	enum	NA	NA	All	Specifies the gender restriction for the intervention. Possible values are: Male Female All	{ "Target_Gender": "Male" }
Target_Residents_Only	boolean	NA	NA	0	When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).	{ "Target_Residents_Only": 1 }
Trigger_Condition_List	enum	NA	NA	NA	This parameter configures NodeLevelHealthTriggeredIV to listen for multiple events at once. The events contained in the list can be built-in events (see Event list for possible events) or the custom events defined in Listed_Events in the simulation configuration file.	{ "Trigger_Condition_List": [ "OnART3" ] }

{
    "Use_Defaults": 1,
    "Events": [{
        "class": "CampaignEvent",
        "Start_Day": 1,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "class": "NodeLevelHealthTriggeredIV",
                "Trigger_Condition_List": ["HappyBirthday"],
                "Demographic_Coverage": 1.0,
                "Actual_IndividualIntervention_Config": {
                    "class": "OutbreakIndividual",
                    "Antigen": 0,
                    "Genome": 0,
                    "Outbreak_Source": "PrevalenceIncrease"
                }
            }
        }
    }]
}

NodeLevelHealthTriggeredIVScaleUpSwitch¶

The NodeLevelHealthTriggeredIVScaleUpSwitch intervention class transitions from one intervention to another over time. Generally this is used if one type of diagnostic tool is being phased out but the transition to replace it with the new diagnostic takes place over a few years. The individuals who are included by Demographic_Coverage will receive the new intervention and those that aren’t will receive the older “not covered” intervention.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Actual_IndividualIntervention_Config	JSON object	NA	NA	NA	The configuration of an actual individual intervention sought. Selects a class for the intervention and configures the parameters specific for that intervention class.	{ "Actual_IndividualIntervention_Config": { "class": "OutbreakIndividual", "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease" } }
Actual_NodeIntervention_Config	JSON object	NA	NA	NA	The configuration of the actual node-level intervention sought. This parameter selects a class for the intervention and configures the parameters specific for that intervention class.	{ "Actual_NodeIntervention_Config": { "class": "OutbreakIndividual", "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease" } }
Blackout_Event_Trigger	enum	NA	NA	NoTrigger	The event to broadcast if an intervention cannot be distributed due to the Blackout_Period. See Event list for possible values.	{ "Blackout_Event_Trigger" : "Blackout" }
Blackout_On_First_Occurrence	boolean	NA	NA	0	If set to true (1), individuals will enter the blackout period after the first occurrence of an event in the Trigger_Condition_List.	{ "Blackout_On_First_Occurrence": 0 }
Blackout_Period	float	0	3.40E+3	0	After the initial intervention distribution, the time, in days, to wait before distributing the intervention again. If it cannot distribute due to the blackout period, it will broadcast the user-defined Blackout_Event_Trigger.	{ "Blackout_Period" : 0.0 }
Demographic_Coverage	float	0	1	1	The fraction of individuals in the target demographic that will receive this intervention.	{ "Demographic_Coverage": 1 }
Demographic_Coverage_Time_Profile	enum	NA	NA	Immediate	The profile for the ramp-up time to demographic coverage. Possible values are: Immediate Linear Sigmoid	{ "Demographic_Coverage_Time_Profile": "Linear" }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Distribute_On_Return_Home	boolean	NA	NA	0	When set to true (1), individuals will receive an intervention upon returning home if that intervention was originally distributed while the individual was away.	{ "Distribute_On_Return_Home" : 1 }
Duration	float	-1	3.40E+3	-1	The number of days to continue this intervention. Note For BirthTriggeredIV, specifying a value of -1 results in indefinite persistence of the birth-triggered intervention.	{ "Duration": -1 }
Initial_Demographic_Coverage	float	0	1.00E+0	0	The initial level of demographic coverage when using linear scale-up.	{ "Initial_Demographic_Coverage": 0 }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Node_Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information. You can specify AND and OR combinations of key:value pairs with this parameter.	The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk. { "Node_Property_Restrictions": [{ "Place": "URBAN", "Risk": "MED" }, { "Place": "RURAL", "Risk": "LOW" } ] }
Not_Covered_IndividualIntervention_Configs	array of JSON objects	NA	NA	NA	The array of interventions that is distributed if an individual qualifies according to all parameters except Demographic_Coverage. Generally, this is used to specify a diagnostic tool that is being phased out.	{ "Not_Covered_IndividualIntervention_Configs": [{ "class": "DelayedIntervention", "Delay_Distribution": "FIXED_DURATION", "Delay_Period": 10, "Coverage": 1, "Actual_IndividualIntervention_Configs": [{ "class": "AntiTBDrug", "Cost_To_Consumer": 90, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 180, "Remaining_Doses": 1, "TB_Drug_Clearance_Rate": 5e-11, "TB_Drug_Inactivation_Rate": 0 }] }] }
Primary_Time_Constant	float	0	3.65E+0	1	The time to full scale-up demographic coverage.	{ "Primary_Time_Constant": 150 }
Property_Restrictions	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.	{ "Property_Restrictions": [ "Risk:HIGH" ] }
Property_Restrictions_Within_Node	array of JSON objects	NA	NA	NA	A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information. This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.	The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk. { "Property_Restrictions_Within_Node": [ {"Risk": "HIGH", "Geographic": "URBAN"}, {"Risk": "MEDIUM", "Geographic": "URBAN"} ] }
Target_Age_Max	float	0	3.40E+3	3.40E+38	The upper end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Demographic	enum	NA	NA	Everyone	The target demographic group. Possible values are: Everyone ExplicitAgeRanges ExplicitAgeRangesAndGender ExplicitGender PossibleMothers ArrivingAirTravellers DepartingAirTravellers ArrivingRoadTravellers DepartingRoadTravellers AllArrivingTravellers AllDepartingTravellers ExplicitDiseaseState	{ "Target_Age_Max": 20, "Target_Age_Min": 10, "Target_Demographic": "ExplicitAgeRanges" }
Target_Gender	enum	NA	NA	All	Specifies the gender restriction for the intervention. Possible values are: Male Female All	{ "Target_Gender": "Male" }
Target_Residents_Only	boolean	NA	NA	0	When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).	{ "Target_Residents_Only": 1 }
Trigger_Condition_List	enum	NA	NA	NA	This parameter configures NodeLevelHealthTriggeredIV to listen for multiple events at once. The events contained in the list can be built-in events (see Event list for possible events) or the custom events defined in Listed_Events in the simulation configuration file.	{ "Trigger_Condition_List": [ "OnART3" ] }

{
    "Use_Defaults": 1,
    "Campaign_Name": "Illustration of NodeLevelScaleUp: Outbreak to smear- and smear+ at day 100, then diagnostic and treatment of only smear+ cases at day 200",
    "Events": [{
        "Event_Name": "when someone broadcasts a positive test using smear, give them the drug",
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 99,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Demographic_Coverage": 1,
            "Include_Arrivals": 0,
            "Include_Departures": 0,
            "Number_Repetitions": 1,
            "Property_Restrictions": [],
            "Target_Demographic": "Everyone",
            "Timesteps_Between_Repetitions": -1,
            "Intervention_Config": {
                "class": "NodeLevelHealthTriggeredIVScaleUpSwitch",
                "Demographic_Coverage": 1,
                "Demographic_Coverage_Time_Profile": "Linear",
                "Initial_Demographic_Coverage": 0,
                "Primary_Time_Constant": 150,
                "Property_Restrictions_Within_Node": [],
                "Not_Covered_IndividualIntervention_Configs": [{
                    "class": "DelayedIntervention",
                    "Delay_Distribution": "FIXED_DURATION",
                    "Delay_Period": 10,
                    "Coverage": 1,
                    "Actual_IndividualIntervention_Configs": [{
                        "class": "SmearDiagnostic",
                        "Base_Sensitivity": 1,
                        "Base_Specificity": 1,
                        "Cost_To_Consumer": 10,
                        "Days_To_Diagnosis": 0,
                        "Treatment_Fraction": 1,
                        "Event_Or_Config": "Event",
                        "Positive_Diagnosis_Event": "TestPositiveOnSmear"
                    }]
                }],
                "Actual_IndividualIntervention_Config": {
                    "class": "ActiveDiagnostic",
                    "Base_Sensitivity": 1,
                    "Base_Specificity": 1,
                    "Cost_To_Consumer": 8,
                    "Days_To_Diagnosis": 0,
                    "Treatment_Fraction": 1,
                    "Event_Or_Config": "Event",
                    "Positive_Diagnosis_Event": "TestPositiveOnActive"
                }
            }
        }
    }]
}

NodePropertyValueChanger¶

The NodePropertyValueChanger intervention class sets a given node property to a new value. You can also define a duration in days before the node property reverts back to its original value, the probability that a node will change its node property to the target value, and the number of days over which nodes will attempt to change their individual properties to the target value. This node-level intervention functions in a similar manner as the individual-level intervention, PropertyValueChanger.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Daily_Probability	float	0	1	1	The probability that an individual will move to the Target_Property_Value.	{ "Intervention_Config": { "class": "PropertyValueChanger", "Disqualifying_Properties": [], "New_Property_Value": "", "Target_Property_Key": "Risk", "Target_Property_Value": "LOW", "Daily_Probability": 1.0, "Maximum_Duration": 0, "Revert": 0 } }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
Maximum_Duration	float	-1	3.40E+3	3.40E+38	The maximum amount of time individuals have to move to a new group. This timing works in conjunction with Daily_Probability.	{ "Intervention_Config": { "class": "PropertyValueChanger", "Disqualifying_Properties": [], "New_Property_Value": "", "Target_Property_Key": "Risk", "Target_Property_Value": "LOW", "Daily_Probability": 1.0, "Maximum_Duration": 0, "Revert": 0 } }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Revert	float	0	10000	0	The number of days before an individual moves back to their original group.	{ "Intervention_Config": { "class": "PropertyValueChanger", "Disqualifying_Properties": [], "New_Property_Value": "", "Target_Property_Key": "Risk", "Target_Property_Value": "LOW", "Daily_Probability": 1.0, "Maximum_Duration": 0, "Revert": 0 } }
Target_NP_Key_Value	string	NA	NA	NA	The NodeProperty key:value pair, as defined in the demographics file, to assign to the node.	{ "Target_NP_Key_Value": "InterventionStatus:NONE" }

{
    "Use_Defaults": 1,
    "Events": [{
        "class": "CampaignEvent",
        "Start_Day": 40,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Node_Property_Restrictions": [{
                "InterventionStatus": "VACCINATING"
            }],
            "Intervention_Config": {
                "class": "NodePropertyValueChanger",
                "Target_NP_Key_Value": "InterventionStatus:STOP_VACCINATING",
                "Daily_Probability": 1.0,
                "Maximum_Duration": 0,
                "Revert": 0
            }
        }
    }]
}

Outbreak¶

The Outbreak class allows the introduction of a disease outbreak event by the addition of infected individuals or by the infection of existing individuals in the specified node set. The type of disease outbreak to be added must be configured. Outbreak is a node-level intervention; to distribute an outbreak intervention to specific categories of individuals within a targeted node, use OutbreakIndividual.

Warning

The event containing the outbreak “intervention” must be the last one listed in the campaign file or none of the disease control interventions will take place.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Antigen	integer	0	10	0	The antigenic ID of the outbreak infection.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease", "class": "OutbreakIndividual" } }
Genome	integer	-2.15E+0	2.15E+0	0	The genetic ID of the outbreak infection.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease", "class": "OutbreakIndividual", "Incubation_Period_Override": 0 } }
Import_Age	float	0	43800	365	The age (in days) of infected import cases.	{ "Import_Age": 10000 }
Incubation_Period_Override	integer	-1	2.15E+0	-1	The incubation period, in days, that infected individuals will go through before becoming infectious. This value overrides the incubation period set in the configuration file. Set to -1 to honor the configuration parameter settings.	{ "Incubation_Period_Override": 0 }
Number_Cases_Per_Node	integer	0	2.15E+0	1	The number of new cases of Outbreak to import (per node). Note This will increase the population and there is no control over demographics of these individuals.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Outbreak_Source": "ImportCases", "Number_Cases_Per_Node": 10, "class": "Outbreak" } }

{
    "Use_Defaults": 1,
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 40,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "class": "Outbreak",
                "Antigen": 0,
                "Genome": 0,
                "Number_Cases_Per_Node": 10
            }
        }
    }]
}

Individual-level interventions¶

Individual-level interventions determine what will be distributed to individuals to reduce the spread of a disease. For example, distributing vaccines or drugs are individual-level interventions. Sometimes this can be an intermediate intervention that schedules another intervention.

The following individual-level interventions are available for use with the tuberculosis simulation type.

ActiveDiagnostic¶

The ActiveDiagnostic intervention class uses the same parameters as SimpleDiagnostic, but it is used specifically to identify individuals with active TB. As with SimpleDiagnostic, Base_Sensitivity and Base_Specificity are used to determine the individual’s test result. Typical usage includes one-time or repeated checks for active TB in the general population.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Base_Sensitivity	float	0	1	1	The sensitivity of the diagnostic. This sets the proportion of the time that individuals with the condition being tested receive a positive diagnostic test. When set to 1, the diagnostic always accurately reflects the condition. When set to zero, then individuals who have the condition always receive a false-negative diagnostic test.	{ "Base_Sensitivity": 0.8 }
Base_Specificity	float	0	1	1	The specificity of the diagnostic. This sets the proportion of the time that individuals without the condition being tested receive a negative diagnostic test. When set to 1, the diagnostic always accurately reflects the lack of having the condition. When set to zero, then individuals who do not have the condition always receive a false-positive diagnostic test.	{ "Base_Specificity": 0.9 }
Cost_To_Consumer	float	0	3.40E+3	1	The unit ‘cost’ assigned to the diagnostic. Setting Cost_To_Consumer to zero for all other interventions, and to a non-zero amount for one intervention, provides a convenient way to track the number of times the intervention has been applied in a simulation.	{ "Cost_To_Consumer": 0.333 }
Days_To_Diagnosis	float	0	3.40E+3	0	The number of days from test until diagnosis.	{ "Days_To_Diagnosis": 0.0 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Event_Or_Config	enum	NA	NA	Config	Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are: Event Config	{ "Event_Or_Config": "Config" }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Positive_Diagnosis_Config	JSON object	NA	NA	NA	The intervention distributed to individuals if they test positive. Only used when Event_Or_Config is set to Config.	{ "Positive_Diagnosis_Config": { "class": "MultiInterventionDistributor", "Intervention_List": [{ "Cost_To_Consumer": 0.333, "Secondary_Decay_Time_Constant": 1, "Vaccine_Take": 1, "Vaccine_Type": "AcquisitionBlocking", "class": "SimpleVaccine", "Waning_Config": { "Box_Duration": 3650, "Initial_Effect": 0.1, "class": "WaningEffectBox" } }] } }
Positive_Diagnosis_Event	enum	NA	NA	No_Trigger	If the test is positive, this specifies an event that can trigger another intervention when the event occurs. Only used if Event_Or_Config is set to Event. See Event list for possible values.	{ "Intervention_Config": { "Base_Sensitivity": 1.0, "Base_Specificity": 1.0, "Cost_To_Consumer": 0.0, "Days_To_Diagnosis": 0.0, "Event_Or_Config": "Event", "Positive_Diagnosis_Event": "TestedPositive_CureMeNow", "Treatment_Fraction": 1.0, "class": "SimpleDiagnostic" } }
Treatment_Fraction	float	0	1	1	The fraction of positive diagnoses that are treated.	{ "Intervention_Config": { "Base_Sensitivity": 1.0, "Base_Specificity": 1.0, "Cost_To_Consumer": 0.0, "Days_To_Diagnosis": 0.0, "Event_Or_Config": "Event", "Positive_Diagnosis_Event": "TestedPositive_CureMeNow", "Treatment_Fraction": 1.0, "class": "SimpleDiagnostic" } }

{
    "Events": [
        {
            "class": "CampaignEvent",
            "Event_Name": "Drugs after TB activation",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 100,
            "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
                "Number_Repetitions": 1,
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1,
                "Intervention_Config": {
                    "class": "ActiveDiagnostic",
                    "Treatment_Fraction": 1,
                    "Base_Sensitivity": 1,
                    "Base_Specificity": 1,
                    "Cost_To_Consumer": 1,
                    "Days_To_Diagnosis": 0,
                    "Event_Or_Config": "Config",
                    "Positive_Diagnosis_Config": {
                        "Cost_To_Consumer": 1,
                        "Dose_Interval": 1,
                        "Drug_Type": "FirstLineCombo",
                        "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT",
                        "Fraction_Defaulters": 0,
                        "Primary_Decay_Time_Constant": 90,
                        "Reduced_Transmit": 0.9,
                        "Remaining_Doses": 1,
                        "TB_Drug_Clearance_Rate": 1e-09,
                        "TB_Drug_Inactivation_Rate": 1e-09,
                        "TB_Drug_Mortality_Rate": 1e-09,
                        "TB_Drug_Relapse_Rate": 1e-09,
                        "TB_Drug_Resistance_Rate": 0.1,
                        "class": "AntiTBDrug"
                    }
                }
            }
        }
    ],
    "Use_Defaults": 1
}

AntiTBDrug¶

The AntiTBDrug intervention class specifies how TB drugs affect individuals in the model. After treatment, individuals may achieve true cure, return to the latent state but quickly relapse, return completely to the latent state, die during treatment, or fail treatment completely.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Cost_To_Consumer	float	0	99999	1	The unit cost per drug (unamortized).	{ "Cost_To_Consumer": 10 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Dose_Interval	float	0	99999	1	The interval between doses, in days.	{ "Dose_Interval": 1 }
Drug_Cmax	float	0	10000	1	The maximum drug concentration that can be used, and is in the same units as Drug_PKPD_C50. Since drug concentrations and C50 values are often specified with different unit systems, these two parameters have been designed to be flexible provided that the units are the same for each. This parameter is used when Durability_Profile is set to CONCENTRATION_VERSUS_TIME.	{ "Durability_Profile": "CONCENTRATION_VERSUS_TIME", "Drug_Cmax": 200, "Drug_PKPD_C50": 6, "Drug_Vd": 1 }
Drug_PKPD_C50	float	0	10000	1	The concentration at which drug killing rates are half of the maximum. Must use the same units as Drug_Cmax. This parameter is used when Durability_Profile is set to CONCENTRATION_VERSUS_TIME.	{ "Durability_Profile": "CONCENTRATION_VERSUS_TIME", "Drug_Cmax": 200, "Drug_PKPD_C50": 6, "Drug_Vd": 1 }
Drug_Type	enum	NA	NA	DOTS	Specifies the name of the drug treatment, such as DOTS or EmpiricTreatment, which can be useful for reporting from the simulation. Possible values are: DOTS DOTSImproved EmpiricTreatment FirstLineCombo SecondLineCombo ThirdLineCombo LatentTreatment	{ "Intervention_Config": { "Cost_To_Consumer": 1, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 10, "Remaining_Doses": 1, "TB_Drug_Clearance_Rate": 0.1, "TB_Drug_Inactivation_Rate": 0.00000001, "TB_Drug_Resistance_Rate": 0.000000001, "TB_Drug_Relapse_Rate": 0.0000001, "Reduced_Transmit": 1, "class": "AntiTBDrug" } }
Drug_Vd	float	0	10000	1	The volume of drug distribution. This value is the ratio of the volume of the second compartment to the volume of the first compartment in a two-compartment model, and is dimensionless. This parameter is used when Durability_Profile is set to CONCENTRATION_VERSUS_TIME.	{ "Durability_Profile": "CONCENTRATION_VERSUS_TIME", "Drug_Cmax": 200, "Drug_PKPD_C50": 6, "Drug_Vd": 1 }
Durability_Profile	enum	NA	NA	FIXED_DURATION_CONSTANT_EFFECT	The profile of durability decay. Possible values are: FIXED_DURATION_CONSTANT_EFFECT The durability does not decay. CONCENTRATION_VERSUS_TIME Conditionally loads the following parameters: Drug_Cmax, Drug_PKPD_C50, and Drug_Vd.	{ "Durability_Profile": "CONCENTRATION_VERSUS_TIME", "Drug_Cmax": 200, "Drug_PKPD_C50": 6, "Drug_Vd": 1 }
Fraction_Defaulters	float	0	1	0	The fraction of individuals who will not finish their drug treatment.	{ "Fraction_Defaulters": 0 }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Primary_Decay_Time_Constant	float	0	1.00E+0	0	The primary decay time constant (in days) of the decay profile.	{ "Primary_Decay_Time_Constant": 180 }
Reduced_Transmit	float	0	1	1	The transmission reduction ratio, or the reduced infectiousness when an individual receives drugs.	{ "Intervention_Config": { "Cost_To_Consumer": 1, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 10, "Remaining_Doses": 1, "TB_Drug_Clearance_Rate": 0.1, "TB_Drug_Inactivation_Rate": 0.00000001, "TB_Drug_Resistance_Rate": 0.000000001, "TB_Drug_Relapse_Rate": 0.0000001, "Reduced_Transmit": 1, "class": "AntiTBDrug" } }
Remaining_Doses	integer	0	999999	0	The remaining doses in an intervention; enter a negative number for unlimited doses.	{ "Remaining_Doses": 1 }
Secondary_Decay_Time_Constant	float	0	999999	1	The secondary decay time constant of the durability profile. This parameter is used when Durability_Profile is set to CONCENTRATION_VERSUS_TIME.	{ "Durability_Profile": "CONCENTRATION_VERSUS_TIME", "Secondary_Decay_Time_Constant": 730 }
TB_Drug_Clearance_Rate	float	0	1	0	The rate of TB clearance per day when an individual is on a drug regimen. This parameter, TB_Drug_Inactivation, TB_Drug_Relapse_Rate, and TB_Drug_Mortality_Rate specify the daily probability of a change in the TB disease state during the course of treatment. The sum of these rates and the total duration of treatment specify the probability that any event occurs. If no event occurs, the individual fails drug treatment.	{ "Actual_IndividualIntervention_Configs": [{ "class": "AntiTBDrug", "Cost_To_Consumer": 90, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 180, "Remaining_Doses": 1, "TB_Drug_Clearance_Rate": 0.05, "TB_Drug_Inactivation_Rate": 0, "TB_Drug_Mortality_Rate": 0.00162, "TB_Drug_Resistance_Rate": 1e-09, "TB_Drug_Relapse_Rate": 0.00223 }] }
TB_Drug_Inactivation_Rate	float	0	1	0	The rate of TB inactivation per day when an individual is on a drug regimen. This parameter, TB_Drug_Clearance_Rate, TB_Drug_Relapse_Rate, and TB_Drug_Mortality_Rate, specify the daily probability of a change in the TB disease state during the course of treatment. The sum of these rates and the total duration of treatment specify the probability that any event occurs. If no event occurs, the individual fails drug treatment.	{ "Actual_IndividualIntervention_Configs": [{ "class": "AntiTBDrug", "Cost_To_Consumer": 90, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 180, "Remaining_Doses": 1, "TB_Drug_Clearance_Rate": 0.05, "TB_Drug_Inactivation_Rate": 0, "TB_Drug_Mortality_Rate": 0.00162, "TB_Drug_Resistance_Rate": 1e-09, "TB_Drug_Relapse_Rate": 0.00223 }] }
TB_Drug_Mortality_Rate	float	0	1	0	The rate of TB mortality per day when an individual is on a drug regimen. This parameter, TB_Drug_Inactivation, TB_Drug_Relapse_Rate, and TB_Drug_Clearance_Rate specify the daily probability of a change in the TB disease state during the course of treatment. The sum of these rates and the total duration of treatment specify the probability that any event occurs. If no event occurs, the individual fails drug treatment.	{ "Actual_IndividualIntervention_Configs": [{ "class": "AntiTBDrug", "Cost_To_Consumer": 90, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 180, "Remaining_Doses": 1, "TB_Drug_Clearance_Rate": 0.05, "TB_Drug_Inactivation_Rate": 0, "TB_Drug_Mortality_Rate": 0.00162, "TB_Drug_Resistance_Rate": 1e-09, "TB_Drug_Relapse_Rate": 0.00223 }] }
TB_Drug_Relapse_Rate	float	0	1	0	The rate of TB relapse per day when an individual is on a drug regimen. This parameter, TB_Drug_Inactivation, TB_Drug_Clearance_Rate, and TB_Drug_Mortality_Rate specify the daily probability of a change in the TB disease state during the course of treatment. The sum of these rates and the total duration of treatment specify the probability that any event occurs. If no event occurs, the individual fails drug treatment.	{ "Actual_IndividualIntervention_Configs": [{ "class": "AntiTBDrug", "Cost_To_Consumer": 90, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 180, "Remaining_Doses": 1, "TB_Drug_Clearance_Rate": 0.05, "TB_Drug_Inactivation_Rate": 0, "TB_Drug_Mortality_Rate": 0.00162, "TB_Drug_Resistance_Rate": 1e-09, "TB_Drug_Relapse_Rate": 0.00223 }] }
TB_Drug_Resistance_Rate	float	0	1	0	This parameter determines the daily probability that an individual with drug-sensitive TB will acquire MDR-TB. Only individuals who return to the latent state or fail can acquire MDR-TB.	{ "Actual_IndividualIntervention_Configs": [{ "class": "AntiTBDrug", "Cost_To_Consumer": 90, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 180, "Remaining_Doses": 1, "TB_Drug_Clearance_Rate": 0.05, "TB_Drug_Inactivation_Rate": 0, "TB_Drug_Mortality_Rate": 0.00162, "TB_Drug_Resistance_Rate": 1e-09, "TB_Drug_Relapse_Rate": 0.00223 }] }

{
    "Campaign_Name": "Basic TB Treatment",
    "Events": [
        {
            "class": "CampaignEvent",
            "Event_Name": "AntiTBDrug",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 3,
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Number_Distributions": -1,
                "Number_Repetitions": 1,
                "Property_Restrictions": [],
                "Target_Group": "Everyone",
                "Timesteps_Between_Repetitions": 1,
                "Demographic_Coverage": 1.0,
                "Intervention_Config": {
                    "class": "AntiTBDrug",
                    "Primary_Decay_Time_Constant": 180,
                    "Reduced_Transmit": 1,
                    "Remaining_Doses": 1,
                    "TB_Drug_Clearance_Rate": 1,
                    "TB_Drug_Inactivation_Rate": 1,
                    "TB_Drug_Mortality_Rate": 1e-09,
                    "TB_Drug_Name": "DOTS",
                    "TB_Drug_Resistance_Rate": 0,
                }
            }
        }
    ],
    "Use_Defaults": 1
}

AntiTBPropDepDrug¶

The AntiTBPropDepDrug intervention class is similar to AntiTBDrug, as it specifies how TB drugs affect individuals in the model. This intervention enables the simultaneous distribution of different drug treatments to specific individuals within the population based on those individuals’ IndividualProperties (see NodeProperties and IndividualProperties parameters for more information). Further, drugs can have different efficacies based on whether the individual is drug-resistant or whether the individual is treatment-experienced. All drugs must be configured in the configuration file under TB_Drug_Types_For_This_Sim and TB_Drug_Params (see Drugs and treatments parameters for more information).

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Cost_To_Consumer	float	0	99999	1	The unit cost per drug (unamortized).	{ "Cost_To_Consumer": 10 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Dose_Interval	float	0	99999	1	The interval between doses, in days.	{ "Dose_Interval": 1 }
Drug_Cmax	float	0	10000	1	The maximum drug concentration that can be used, and is in the same units as Drug_PKPD_C50. Since drug concentrations and C50 values are often specified with different unit systems, these two parameters have been designed to be flexible provided that the units are the same for each. This parameter is used when Durability_Profile is set to CONCENTRATION_VERSUS_TIME.	{ "Durability_Profile": "CONCENTRATION_VERSUS_TIME", "Drug_Cmax": 200, "Drug_PKPD_C50": 6, "Drug_Vd": 1 }
Drug_PKPD_C50	float	0	10000	1	The concentration at which drug killing rates are half of the maximum. Must use the same units as Drug_Cmax. This parameter is used when Durability_Profile is set to CONCENTRATION_VERSUS_TIME.	{ "Durability_Profile": "CONCENTRATION_VERSUS_TIME", "Drug_Cmax": 200, "Drug_PKPD_C50": 6, "Drug_Vd": 1 }
Drug_Type_by_Property	array of JSON objects	NA	NA	na	An array of JSON objects that defines the drugs to distribute based on individual properties. Each JSON object is in the format “property:value”: “drug”. The property values are defined in the demographics file (NodeProperties and IndividualProperties) using IndividualProperties and the drugs are defined in the configuration file (Drugs and treatments) using TB_Drug_Types_For_This_Sim and TB_Drug_Params.	{ "Drug_Type_by_Property": [{ "QualityOfCare:CDC": "CDCDrug" }, { "QualityOfCare:Hospital": "HospitalDrug" } ] }
Drug_Vd	float	0	10000	1	The volume of drug distribution. This value is the ratio of the volume of the second compartment to the volume of the first compartment in a two-compartment model, and is dimensionless. This parameter is used when Durability_Profile is set to CONCENTRATION_VERSUS_TIME.	{ "Durability_Profile": "CONCENTRATION_VERSUS_TIME", "Drug_Cmax": 200, "Drug_PKPD_C50": 6, "Drug_Vd": 1 }
Durability_Profile	enum	NA	NA	FIXED_DURATION_CONSTANT_EFFECT	The profile of durability decay. Possible values are: FIXED_DURATION_CONSTANT_EFFECT The durability does not decay. CONCENTRATION_VERSUS_TIME Conditionally loads the following parameters: Drug_Cmax, Drug_PKPD_C50, and Drug_Vd.	{ "Durability_Profile": "CONCENTRATION_VERSUS_TIME", "Drug_Cmax": 200, "Drug_PKPD_C50": 6, "Drug_Vd": 1 }
Enable_State_Specific_Treatment	boolean	NA	NA	1	If an individual develops MDR-TB and is getting the default drug, the intervention automatically starts giving an MDR drug with different treatment outcomes when set to true (1). If this is enabled, the MDR drugs must be included in the configuration parameter TB_Drug_Params list with the same name as the original drug, but with MDR or Retx appended to the end. For example, for a “HospitalDrug”, you would also need to parameterize “HospitalDrugMDR” and “HospitalDrugRetx”.	{ "Enable_State_Specific_Treatment": 1 }
Fraction_Defaulters	float	0	1	0	The fraction of individuals who will not finish their drug treatment.	{ "Fraction_Defaulters": 0 }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Primary_Decay_Time_Constant	float	0	1.00E+0	0	The primary decay time constant (in days) of the decay profile.	{ "Primary_Decay_Time_Constant": 180 }
Reduced_Transmit	float	0	1	1	The transmission reduction ratio, or the reduced infectiousness when an individual receives drugs.	{ "Intervention_Config": { "Cost_To_Consumer": 1, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 10, "Remaining_Doses": 1, "TB_Drug_Clearance_Rate": 0.1, "TB_Drug_Inactivation_Rate": 0.00000001, "TB_Drug_Resistance_Rate": 0.000000001, "TB_Drug_Relapse_Rate": 0.0000001, "Reduced_Transmit": 1, "class": "AntiTBDrug" } }
Remaining_Doses	integer	0	999999	0	The remaining doses in an intervention; enter a negative number for unlimited doses.	{ "Remaining_Doses": 1 }
Secondary_Decay_Time_Constant	float	0	999999	1	The secondary decay time constant of the durability profile. This parameter is used when Durability_Profile is set to CONCENTRATION_VERSUS_TIME.	{ "Durability_Profile": "CONCENTRATION_VERSUS_TIME", "Secondary_Decay_Time_Constant": 730 }

{
    "Campaign_Name": "Campaign - Outbreak, drug-seeking on TBActivation",
    "Events": [
        {
            "class": "CampaignEvent",
            "Event_Name": "Drugs after TB activation",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 100,
            "Event_Coordinator_Config": {
                "class": "GroupInterventionDistributionEventCoordinator",
                "Target_Demographic": "ExplicitDiseaseState",
                "Number_Repetitions": 1,
                "Property_Restrictions": [],
                "Target_Disease_State": "Infected",
                "Intervention_Config": {
                    "class": "AntiTBPropDepDrug",
                    "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT",
                    "Remaining_Doses": 1,
                    "Cost_To_Consumer": 1,
                    "Enable_State_Specific_Treatment": 1,
                    "Drug_Type_by_Property": [
                        { "QualityOfCare:CDC":"CDCDrug" },
                        { "QualityOfCare:Hospital": "HospitalDrug" }
                    ]
                }
            }
        }
    ],
    "Use_Defaults": 1
}

BCGVaccine¶

The BCGVaccine intervention class includes vaccination in the simulation. Vaccination can reduce the likelihood of infection or transmission of pathogens. This intervention adds further functionality to vaccination by including the ability to vary vaccine take with age.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Cost_To_Consumer	float	0	999999	10	The unit cost per vaccine (unamortized).	{ "Cost_To_Consumer": 10.0 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Efficacy_Is_Multiplicative	boolean	NA	NA	1	The overall vaccine efficacy when individuals receive more than one vaccine. When set to true (1), the vaccine efficacies are multiplied together; when set to false (0), the efficacies are additive.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Primary_Decay_Time_Constant	float	0	1.00E+0	0	The primary decay time constant (in days) of the decay profile.	{ "Primary_Decay_Time_Constant": 180 }
Reduced_Transmit	float	0	1	1	The transmission reduction ratio, or the reduced infectiousness when an individual receives drugs.	{ "Intervention_Config": { "Cost_To_Consumer": 1, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 10, "Remaining_Doses": 1, "TB_Drug_Clearance_Rate": 0.1, "TB_Drug_Inactivation_Rate": 0.00000001, "TB_Drug_Resistance_Rate": 0.000000001, "TB_Drug_Relapse_Rate": 0.0000001, "Reduced_Transmit": 1, "class": "AntiTBDrug" } }
Secondary_Decay_Time_Constant	float	0	999999	1	The secondary decay time constant of the durability profile. This parameter is used when Durability_Profile is set to CONCENTRATION_VERSUS_TIME.	{ "Durability_Profile": "CONCENTRATION_VERSUS_TIME", "Secondary_Decay_Time_Constant": 730 }
Vaccine_Take	float	0	1	1	The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Vaccine_Take_Age_Decay_Rate	float	-3.40E+3	3.40E+3	1	The exponential decline in vaccine take over time. The rate of decline is in units of 1/years.	{ "Vaccine_Take_Age_Decay_Rate": 0.2 }
Vaccine_Type	enum	NA	NA	Generic	The type of vaccine to distribute in a vaccine intervention. Possible values are: Generic The vaccine can reduce transmission, acquisition, and mortality. TransmissionBlocking The vaccine will reduce pathogen transmission. AcquisitionBlocking The vaccine will reduce the acquisition of the pathogen by reducing the force of infection experienced by the vaccinated individual. MortalityBlocking The vaccine reduces the disease-mortality rate of a vaccinated individual.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Waning_Config	JSON object	NA	NA	NA	The configuration of how the intervention efficacy wanes over time. Specify how this effect decays over time using one of the Waning effect classes.	{ "Waning_Config": { "Box_Duration": 3650, "Initial_Effect": 1, "class": "WaningEffectBox" } }

{
    "Events": [
        {
            "class": "CampaignEvent",
            "Event_Name": "vaccination for all new births",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 1,
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Number_Repetitions": 1,
                "Property_Restrictions": [],
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1,
                "Include_Arrivals": 0,
                "Include_Departures": 0,
                "Intervention_Config": {
                    "class": "NodeLevelHealthTriggeredIV",
                    "Actual_IndividualIntervention_Config": {
                        "Cost_To_Consumer": 1,
                        "Vaccine_Take": 1,
                        "Vaccine_Take_Age_Decay_Rate": 0,
                        "Vaccine_Type": "AcquisitionBlocking",
                        "class": "BCGVaccine",
                        "Waning_Config": {
                            "class": "WaningEffectBox",
                            "Initial_Effect": 1,
                            "Box_Duration": 5475
                        }
                    },
                    "Trigger_Condition_List": [
                        "Births"
                    ]
                }
            }
        }
    ],
    "Use_Defaults": 1
}

BroadcastEvent¶

The BroadcastEvent intervention class immediately broadcasts the event trigger you specify. This campaign event is typically used with other classes that monitor for a broadcast event, such as NodeLevelHealthTriggeredIV or CommunityHealthWorkerEventCoordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Broadcast_Event	string	NA	NA	No Trigger	The name of the event that will trigger the intervention. See Event list for possible values.	{ "Broadcast_Event": "HIVNeedsHIVTest" }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }

{
    "Use_Defaults": 1,
    "Campaign_Name": "4C: HIVMuxer",
    "Events": [{
            "Description": "Drive initial population into a loop",
            "class": "CampaignEvent",
            "Start_Day": 1,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "class": "BroadcastEvent",
                    "Broadcast_Event": "Loop_Entry_InitialPopulation"
                }
            }
        },
        {
            "Description": "Wait one year, only one entry allowed at a time per individual",
            "class": "CampaignEvent",
            "Start_Day": 1,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "class": "NodeLevelHealthTriggeredIV",
                    "Trigger_Condition_List": [
                        "Loop_Entry_InitialPopulation",
                        "Loop_Entry_Birth",
                        "Done_Waiting"
                    ],
                    "Actual_IndividualIntervention_Config": {
                        "class": "HIVMuxer",
                        "Disqualifying_Properties": ["InterventionStatus:Abort_Infinite_Loop"],
                        "New_Property_Value": "InterventionStatus:Infinite_Loop",
                        "Delay_Distribution": "FIXED_DURATION",
                        "Delay_Period": 365,
                        "Muxer_Name": "Delay_Loop_Muxer",
                        "Max_Entries": 1,
                        "Broadcast_Event": "Done_Waiting"
                    }
                }
            }
        }
    ]
}

BroadcastEventToOtherNodes¶

The BroadcastEventToOtherNodes intervention class allows events to be sent from one node to another. For example, if an individual in one node has been diagnosed, drugs may be distributed to individuals in surrounding nodes.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Event_Trigger	enum	NA	NA	NoTrigger	The name of the event to broadcast to selected nodes. See Event list for possible values.	{ "Event_Trigger": "VaccinateNeighbors" }
Include_My_Node	boolean	NA	NA	0	Set to true (1) to broadcast the event to the current node.	{ "Actual_IndividualIntervention_Config": { "class": "BroadcastEventToOtherNodes", "Event_Trigger": "VaccinateNeighbors", "Include_My_Node" : 1, "Node_Selection_Type" : "DISTANCE_AND_MIGRATION", "Max_Distance_To_Other_Nodes_Km" : 1 } }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
Max_Distance_To_Other_Nodes_Km	float	0	3.40E+3	3.40E+38	The maximum distance, in kilometers, to the destination node for the node to be selected. The location values used are those entered in the demographics file. Used only if Node_Selection_Type is either DISTANCE_ONLY or DISTANCE_AND_MIGRATION.	{ "Actual_IndividualIntervention_Config": { "class": "BroadcastEventToOtherNodes", "Event_Trigger": "VaccinateNeighbors", "Include_My_Node" : 1, "Node_Selection_Type" : "DISTANCE_AND_MIGRATION", "Max_Distance_To_Other_Nodes_Km" : 1 } }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Node_Selection_Type	enum	NA	NA	DISTANCE_ONLY	The method by which to select nodes to receive the event. Possible values are: DISTANCE_ONLY Nodes located within the distance specified by Max_Distance_To_Other_Nodes_Km are selected. MIGRATION_NODES_ONLY Nodes that are local or regional are selected. DISTANCE_AND_MIGRATION Nodes are selected using DISTANCE_ONLY and MIGRATION_NODES_ONLY criteria.	{ "Actual_IndividualIntervention_Config": { "class": "BroadcastEventToOtherNodes", "Event_Trigger": "VaccinateNeighbors", "Include_My_Node" : 1, "Node_Selection_Type" : "DISTANCE_AND_MIGRATION", "Max_Distance_To_Other_Nodes_Km" : 1 } }

{
    "Use_Defaults": 1,
    "Events": [{
            "Event_Name": "Broadcast to Other Households If Person Infected",
            "class": "CampaignEvent",
            "Start_Day": 0,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Demographic_Coverage": 1,
                "Intervention_Config": {
                    "class": "NodeLevelHealthTriggeredIV",
                    "Trigger_Condition_List": ["NewClinicalCase"],
                    "Blackout_Event_Trigger": "Blackout",
                    "Blackout_Period": 0.0,
                    "Blackout_On_First_Occurrence": 0,
                    "Actual_IndividualIntervention_Config": {
                        "class": "BroadcastEventToOtherNodes",
                        "Event_Trigger": "VaccinateNeighbors",
                        "Include_My_Node": 1,
                        "Node_Selection_Type": "DISTANCE_AND_MIGRATION",
                        "Max_Distance_To_Other_Nodes_Km": 1
                    }
                }
            }
        },
        {
            "Event_Name": "Get Vaccinated If Neighbor Infected",
            "class": "CampaignEvent",
            "Start_Day": 0,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Demographic_Coverage": 1,
                "Intervention_Config": {
                    "class": "NodeLevelHealthTriggeredIV",
                    "Trigger_Condition_List": ["VaccinateNeighbors"],
                    "Blackout_Event_Trigger": "Blackout",
                    "Blackout_Period": 0.0,
                    "Blackout_On_First_Occurrence": 0,
                    "Actual_IndividualIntervention_Config": {
                        "class": "AntimalarialDrug",
                        "Cost_To_Consumer": 10,
                        "Dosing_Type": "FullTreatmentParasiteDetect",
                        "Drug_Type": "Chloroquine",
                        "Dont_Allow_Duplicates": 1
                    }
                }
            }
        }
    ]
}

ControlledVaccine¶

The ControlledVaccine intervention class is a subclass of SimpleVaccine so it contains all functionality of SimpleVaccine, but provides more control over additional events and event triggers. This intervention can be configured so that specific events are broadcast when individuals receive an intervention or when the intervention expires. Further, individuals can be re-vaccinated, using a configurable wait time between vaccinations.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Cost_To_Consumer	float	0	999999	10	The unit cost per vaccine (unamortized).	{ "Cost_To_Consumer": 10.0 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Distributed_Event_Trigger	enum	NA	NA	NoTrigger	The name of the event to be broadcast when the intervention is distributed to an individual. See Event list for possible values.	{ "Distributed_Event_Trigger": "Vaccinated" }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Duration_To_Wait_Before_Revaccination	float	0	3.40E+3	3.40E+38	The length of time, in days, to wait before revaccinating an individual. After this time has passed, the individual can be revaccinated. If the first vaccine has not expired, the individual can receive the effect from both doses of the vaccine.	{ "Duration_To_Wait_Before_Revaccination": 240 }
Efficacy_Is_Multiplicative	boolean	NA	NA	1	The overall vaccine efficacy when individuals receive more than one vaccine. When set to true (1), the vaccine efficacies are multiplied together; when set to false (0), the efficacies are additive.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Expired_Event_Trigger	enum	NA	NA	NoTrigger	The name of the event to be broadcast when the intervention is distributed to an individual. See Event list for possible values.	{ "Expired_Event_Trigger": "VaccineExpired" }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Vaccine_Take	float	0	1	1	The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Vaccine_Type	enum	NA	NA	Generic	The type of vaccine to distribute in a vaccine intervention. Possible values are: Generic The vaccine can reduce transmission, acquisition, and mortality. TransmissionBlocking The vaccine will reduce pathogen transmission. AcquisitionBlocking The vaccine will reduce the acquisition of the pathogen by reducing the force of infection experienced by the vaccinated individual. MortalityBlocking The vaccine reduces the disease-mortality rate of a vaccinated individual.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Waning_Config	JSON object	NA	NA	NA	The configuration of how the intervention efficacy wanes over time. Specify how this effect decays over time using one of the Waning effect classes.	{ "Waning_Config": { "Box_Duration": 3650, "Initial_Effect": 1, "class": "WaningEffectBox" } }

{
    "Use_Defaults": 1,
    "Events": [{
            "COMMENT": "Vaccinate Everyone with VaccineA",
            "class": "CampaignEvent",
            "Start_Day": 1,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1.0,
                "Intervention_Config": {
                    "class": "ControlledVaccine",
                    "Intervention_Name": "VaccineA",
                    "Cost_To_Consumer": 1,
                    "Vaccine_Type": "AcquisitionBlocking",
                    "Vaccine_Take": 1.0,
                    "Waning_Config": {
                        "class": "WaningEffectMapLinear",
                        "Initial_Effect": 1.0,
                        "Expire_At_Durability_Map_End": 1,
                        "Durability_Map": {
                            "Times": [0, 25, 50],
                            "Values": [1.0, 1.0, 0.0]
                        }
                    },
                    "Distributed_Event_Trigger": "VaccinatedA",
                    "Expired_Event_Trigger": "VaccineExpiredA",
                    "Duration_To_Wait_Before_Revaccination": 40
                }
            }
        },
        {
            "COMMENT": "After the first round expires, distribute a different vaccine, VaccineB",
            "class": "CampaignEvent",
            "Start_Day": 60,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1.0,
                "Intervention_Config": {
                    "class": "ControlledVaccine",
                    "Intervention_Name": "VaccineB",
                    "Cost_To_Consumer": 1,
                    "Vaccine_Type": "AcquisitionBlocking",
                    "Vaccine_Take": 1.0,
                    "Waning_Config": {
                        "class": "WaningEffectMapLinear",
                        "Initial_Effect": 1.0,
                        "Expire_At_Durability_Map_End": 1,
                        "Durability_Map": {
                            "Times": [0, 25, 50],
                            "Values": [1.0, 1.0, 0.0]
                        }
                    },
                    "Distributed_Event_Trigger": "VaccinatedB",
                    "Expired_Event_Trigger": "VaccineExpiredB",
                    "Duration_To_Wait_Before_Revaccination": 40
                }
            }
        }
    ]
}

DelayedIntervention¶

The DelayedIntervention intervention class introduces a delay between when the intervention is distributed to the individual and when they receive the actual intervention. This is due to the frequent occurrences of time delays as individuals seek care and receive treatment. This intervention allows configuration of the distribution type for the delay as well as the fraction of the population that receives the specified intervention.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Actual_IndividualIntervention_Configs	array of JSON objects	NA	NA	NA	An array of nested interventions to be distributed at the end of a delay period, to covered fraction of the population.	{ "Actual_IndividualIntervention_Configs": [{ "class": "HIVSimpleDiagnostic", "New_Property_Value": "InterventionStatus:None", "Base_Specificity": 1, "Base_Sensitivity": 1, "Event_Or_Config": "Event", "Positive_Diagnosis_Event": "NoTrigger", "Negative_Diagnosis_Event": "HIVNeedsMaleCircumcision" }] }
Coverage	float	0	1	1	The proportion of individuals who receive the DelayedIntervention that actually receive the configured interventions.	{ "Coverage": 1.0 }
Delay_Distribution	enum	NA	NA	NOT_INITIALIZED	The distribution of the time delay from when the intervention is distributed to when the individual actually receives the intervention. Possible values are: NOT_INITIALIZED No distribution set. FIXED_DURATION A constant duration. UNIFORM_DURATION A uniform random draw for the duration. GAUSSIAN_DURATION Duration of the active period is defined by the mean and standard deviation of the Gaussian distribution. Negative values are truncated at zero. EXPONENTIAL_DURATION The active period is the mean of the exponential random draw. POISSON_DURATION The active period is the mean of the random Poisson draw. LOG_NORMAL_DURATION The active period is a log normal distribution defined by a mean and log width. BIMODAL_DURATION The distribution is bimodal, the duration is a fraction of the active period for a specified period of time and equal to the active period otherwise. PIECEWISE_CONSTANT The distribution is specified with a list of years and a matching list of values. The duration at a given year is that specified for the nearest previous year. PIECEWISE_LINEAR The distribution is specified with a list of years and matching list of values. The duration at a given year is a linear interpolation of the specified values. WEIBULL_DURATION The duration is a Weibull distribution with a given scale and shape. DUAL_TIMESCALE_DURATION The duration is two exponential distributions with given means.	{ "Delay_Distribution": "UNIFORM_DURATION" }
Delay_Period	float	0	3.40E+3	6	When Delay_Distribution is set to FIXED_DURATION, this will specify the time delay (in number of days). When Delay_Distribution is set to EXPONENTIAL_DURATION, this represents the exponential rate that describes the distribution of the time delay (in units of 1/days).	{ "Delay_Distribution": "FIXED_DURATION", "Delay_Period": 8 }
Delay_Period_Max	float	0	3.40E+3	6	The maximum time delay (in number of days) when Delay_Distribution is set to UNIFORM_DURATION.	{ "Delay_Distribution": "UNIFORM_DURATION", "Delay_Period_Min": 1, "Delay_Period_Max": 30 }
Delay_Period_Mean	float	0	3.40E+3	6	The mean time delay (in number of days), when Delay_Distribution is set to GAUSSIAN_DURATION.	{ "Delay_Distribution": "GAUSSIAN_DURATION", "Delay_Period_Mean": 25, "Delay_Period_Std_Dev": 5 }
Delay_Period_Min	float	0	3.40E+3	6	The minimum time delay (in number of days) when Delay_Distribution is set to UNIFORM_DURATION.	{ "Delay_Distribution": "UNIFORM_DURATION", "Delay_Period_Min": 1, "Delay_Period_Max": 30 }
Delay_Period_Scale	float	0	3.40E+3	16	The scale parameter (lambda > 0) of the distribution (in days) when Delay_Distribution is set to WEIBULL_DURATION.	{ "Delay_Distribution": "WEIBULL_DURATION", "Delay_Period_Scale": 10, "Delay_Period_Shape": 15 }
Delay_Period_Shape	float	0		20	The shape parameter (kappa > 0) of the distribution when Delay_Distribution is set to WEIBULL_DURATION.	{ "Delay_Distribution": "WEIBULL_DURATION", "Delay_Period_Scale": 10, "Delay_Period_Shape": 15 }
Delay_Period_Std_Dev	float	0	3.40E+3	6	The standard deviation of the Gaussian distribution (in number of days) when Delay_Distribution is set to GAUSSIAN_DURATION.	{ "Delay_Distribution": "GAUSSIAN_DURATION", "Delay_Period_Mean": 25, "Delay_Period_Std_Dev": 5 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }

{
    "Campaign_Name": "Initial Seeding",
    "Events": [{
            "Event_Name": "Outbreak",
            "class": "CampaignEvent",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 1,
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1.0,
                "Intervention_Config": {
                    "class": "DelayedIntervention",
                    "Delay_Distribution": "FIXED_DURATION",
                    "Delay_Period": 25,
                    "Actual_IndividualIntervention_Configs": [{
                        "Outbreak_Source": "PrevalenceIncrease",
                        "class": "OutbreakIndividual"
                    }]
                }
            }
        },
        {
            "Event_Name": "Outbreak",
            "class": "CampaignEvent",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 50,
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1.0,
                "Intervention_Config": {
                    "class": "DelayedIntervention",
                    "Delay_Distribution": "UNIFORM_DURATION",
                    "Delay_Period_Min": 15,
                    "Delay_Period_Max": 30,
                    "Actual_IndividualIntervention_Configs": [{
                        "Outbreak_Source": "PrevalenceIncrease",
                        "class": "OutbreakIndividual"
                    }]
                }
            }
        }
    ],
    "Use_Defaults": 1
}

DiagnosticTreatNeg¶

The DiagnosticTreatNeg intervention class is similar to SimpleDiagnostic, but enables greater specification of what occurs if an individual receives a test (particularly a negative test, which cannot be specified using SimpleDiagnostic or ActiveDiagnostic) or defaults (such as getting tested but not receiving test results).

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Base_Sensitivity	float	0	1	1	The sensitivity of the diagnostic. This sets the proportion of the time that individuals with the condition being tested receive a positive diagnostic test. When set to 1, the diagnostic always accurately reflects the condition. When set to zero, then individuals who have the condition always receive a false-negative diagnostic test.	{ "Base_Sensitivity": 0.8 }
Base_Specificity	float	0	1	1	The specificity of the diagnostic. This sets the proportion of the time that individuals without the condition being tested receive a negative diagnostic test. When set to 1, the diagnostic always accurately reflects the lack of having the condition. When set to zero, then individuals who do not have the condition always receive a false-positive diagnostic test.	{ "Base_Specificity": 0.9 }
Cost_To_Consumer	float	0	3.40E+3	1	The unit ‘cost’ assigned to the diagnostic. Setting Cost_To_Consumer to zero for all other interventions, and to a non-zero amount for one intervention, provides a convenient way to track the number of times the intervention has been applied in a simulation.	{ "Cost_To_Consumer": 0.333 }
Days_To_Diagnosis	float	0	3.40E+3	0	The number of days from test until diagnosis.	{ "Days_To_Diagnosis": 0.0 }
Defaulters_Config	JSON object	NA	NA	NA	The intervention configuration given out when an individual did not return for test results when Event_Or_Config is set to ‘Config’.	{ "Defaulters_Config": { "class": "DelayedIntervention", "Delay_Distribution": "FIXED_DURATION", "Coverage": 1, "Delay_Period": 600, "Actual_IndividualIntervention_Configs": [{ "class": "AntiTBDrug", "Cost_To_Consumer": 90, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 180, "Remaining_Doses": 1, "TB_Drug_Clearance_Rate": 0.05, "TB_Drug_Inactivation_Rate": 0 }] } }
Defaulters_Event	enum	NA	NA	NoTrigger	Specifies an event that can trigger another intervention when the event occurs. Event_Or_Config must be set to ‘Event’, and an individual did not return for test results. See the Event list for possible values.	{ "Intervention_Config": { "class": "MDRDiagnostic", "Base_Sensitivity": 1, "Base_Specificity": 1, "Cost_To_Consumer": 10, "Days_To_Diagnosis": 14, "Treatment_Fraction": 1, "Event_Or_Config": "Event", "Positive_Diagnosis_Event": "TestPositiveOnSmear", "Negative_Diagnosis_Event": "NewSevereCase", "Defaulters_Event": "NewSevereCase" } }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Event_Or_Config	enum	NA	NA	Config	Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are: Event Config	{ "Event_Or_Config": "Config" }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
Negative_Diagnosis_Config	JSON object	NA	NA	NA	If Event_Or_Config is set to Config, this is the intervention given out when there is a negative diagnosis.	{ "Event_Or_Config": "Config", "Negative_Diagnosis_Config": { "class": "DelayedIntervention", "Delay_Distribution": "FIXED_DURATION", "Coverage": 1, "Delay_Period": 250 } }
Negative_Diagnosis_Event	enum	NA	NA	NoTrigger	If an individual tests negative, this specifies an event that may trigger another intervention when the event occurs. Only used when Event_Or_Config is set to Event. See Event list for possible values.	{ "Event_Or_Config": "Event", "Negative_Diagnosis_Event": "NewSevereCase" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Positive_Diagnosis_Config	JSON object	NA	NA	NA	The intervention distributed to individuals if they test positive. Only used when Event_Or_Config is set to Config.	{ "Positive_Diagnosis_Config": { "class": "MultiInterventionDistributor", "Intervention_List": [{ "Cost_To_Consumer": 0.333, "Secondary_Decay_Time_Constant": 1, "Vaccine_Take": 1, "Vaccine_Type": "AcquisitionBlocking", "class": "SimpleVaccine", "Waning_Config": { "Box_Duration": 3650, "Initial_Effect": 0.1, "class": "WaningEffectBox" } }] } }
Positive_Diagnosis_Event	enum	NA	NA	No_Trigger	If the test is positive, this specifies an event that can trigger another intervention when the event occurs. Only used if Event_Or_Config is set to Event. See Event list for possible values.	{ "Intervention_Config": { "Base_Sensitivity": 1.0, "Base_Specificity": 1.0, "Cost_To_Consumer": 0.0, "Days_To_Diagnosis": 0.0, "Event_Or_Config": "Event", "Positive_Diagnosis_Event": "TestedPositive_CureMeNow", "Treatment_Fraction": 1.0, "class": "SimpleDiagnostic" } }
Treatment_Fraction	float	0	1	1	The fraction of positive diagnoses that are treated.	{ "Intervention_Config": { "Base_Sensitivity": 1.0, "Base_Specificity": 1.0, "Cost_To_Consumer": 0.0, "Days_To_Diagnosis": 0.0, "Event_Or_Config": "Event", "Positive_Diagnosis_Event": "TestedPositive_CureMeNow", "Treatment_Fraction": 1.0, "class": "SimpleDiagnostic" } }

{
    "Campaign_Name": "Smear Diagnostic and Treatment with HIV",
    "Events": [
        {
            "class": "CampaignEvent",
            "Event_Name": "Test Everyone for Smear Pos TB",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 5,
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Number_Distributions": -1,
                "Number_Repetitions": 1,
                "Property_Restrictions": [],
                "Target_Group": "Everyone",
                "Timesteps_Between_Repetitions": 1,
                "Demographic_Coverage": 1.0,
                "Intervention_Config": {
                    "class": "DiagnosticTreatNeg",
                    "Base_Sensitivity": 1,
                    "Base_Specificity": 1,
                    "Days_to_Diagnosis": 0,
                    "Defaulters_Event": "Default",
                    "Event_Or_Config": "Event",
                    "Negative_Diagnosis_Event": "Diagnosed_Negative",
                    "Positive_Diagnosis_Event": "Diagnosed_Smear_Pos",
                    "Treatment_Fraction": 1
                }
            }
        },
        {
            "class": "CampaignEvent",
            "Event_Name": "Configurable TB Drug",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 1,
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Number_Distributions": -1,
                "Number_Repetitions": 1,
                "Property_Restrictions": [],
                "Target_Group": "Everyone",
                "Timesteps_Between_Repetitions": 1,
                "Demographic_Coverage": 1.0,
                "Intervention_Config": {
                "class": "NodeLevelHealthTriggeredIV",
                    "Actual_IndividualIntervention_Config": {
                        "Remaining_Doses": 1,
                        "TB_Drug_Name": "ACFDOTS",
                        "class": "TBHIVConfigurableTBdrug"
                    },
                    "Duration": -1,
                    "Trigger_Condition_List": [
                        "Diagnosed_Smear_Pos"
                    ]
                }
            }
        }
    ],
    "Use_Defaults": 1
}

HealthSeekingBehaviorUpdate¶

The HealthSeekingBehaviorUpdate intervention class dynamically updates the probability of care- seeking behaviors. The initial probability of care is set in HealthSeekingBehaviorUpdateable.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
New_Tendency	float	0	1	1	Updates the tendency specified by HealthSeekingBehaviorUpdateable.	{ "New_Tendency": 0.1 }

{
     "Campaign_Name": "Campaign - Outbreak, drug-seeking",
     "Events": [{
               "Event_Coordinator_Config": {
                    "Demographic_Coverage": 1,
                    "Intervention_Config": {
                         "Actual_IndividualIntervention_Config": {
                              "Cost_To_Consumer": 1,
                              "Drug_Type": "FirstLineCombo",
                              "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT",
                              "Primary_Decay_Time_Constant": 180,
                              "Remaining_Doses": 1,
                              "Secondary_Decay_Time_Constant": 0,
                              "TB_Drug_Clearance_Rate": 0.2,
                              "TB_Drug_Inactivation_Rate": 0.000000001,
                              "class": "AntiTBDrug"
                         },
                         "Event_Or_Config": "Config",
                         "Tendency": 0.001,
                         "class": "HealthSeekingBehaviorUpdateable"
                    },
                    "Number_Repetitions": 1,
                    "Target_Demographic": "Everyone",
                    "class": "StandardInterventionDistributionEventCoordinator"
               },
               "Event_Name": "Start HSB",
               "Nodeset_Config": {
                    "class": "NodeSetAll"
               },
               "Start_Day": 20,
               "class": "CampaignEvent"
          },
          {
               "class": "CampaignEvent",
               "Event_Name": "Drugs after TB activation",
               "Nodeset_Config": {
                    "class": "NodeSetAll"
               },
               "Start_Day": 100,
               "Event_Coordinator_Config": {
                    "class": "StandardInterventionDistributionEventCoordinator",
                    "Number_Repetitions": 1,
                    "Target_Demographic": "Everyone",
                    "Demographic_Coverage": 1,
                    "Intervention_Config": {
                         "class": "HealthSeekingBehaviorUpdate",
                         "New_Tendency": 0.1
                    }
               }
          }
     ],
     "Use_Defaults": 1
}

HealthSeekingBehaviorUpdateable¶

The HealthSeekingBehaviorUpdateable intervention class is similar to SimpleHealthSeekingBehavior, but is specific to the TB model. This intervention is used to set an initial rate of seeking care, which can be modified dynamically by HealthSeekingBehaviorUpdate. As an example, this intervention can be used to model the effects of disease awareness campaigns or other interventions that help shorten the time delay between when individuals start experiencing symptoms and when they receive care.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Actual_IndividualIntervention_Config	JSON object	NA	NA	NA	The configuration of an actual intervention sought. Selects a class for the intervention and configures the parameters specific for that intervention class.	{ "Actual_IndividualIntervention_Config": { "Cost_To_Consumer": 1, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 180, "Remaining_Doses": 1, "Secondary_Decay_Time_Constant": 0, "TB_Drug_Clearance_Rate": 0.2, "TB_Drug_Inactivation_Rate": 0.000000001, "class": "AntiTBDrug" } }
Actual_IndividualIntervention_Event	enum	NA	NA	NoTrigger	The event of an actual intervention sought. Selects a class for the intervention and configures the parameters specific for that intervention class. See Event list for possible values.	{ "Actual_IndividualIntervention_Config": { "Actual_IndividualIntervention_Event": "ProviderOrdersTBTest", "Tendency": 0.005, "Event_Or_Config": "Event", "class": "SimpleHealthSeekingBehavior" } }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Event_Or_Config	enum	NA	NA	Config	Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are: Event Config	{ "Event_Or_Config": "Config" }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Single_Use	boolean	NA	NA	1	If set to true (1), the health-seeking behavior gets used once and discarded. If set to false (0), it remains indefinitely.	{ "Single_Use": 1 }
Tendency	float	0	1	1	The probability of seeking healthcare.	{ "Tendency": 0.01 }

{
     "Campaign_Name": "Campaign - Outbreak, drug-seeking",
     "Events": [{
               "Event_Coordinator_Config": {
                    "Demographic_Coverage": 1,
                    "Intervention_Config": {
                         "Actual_IndividualIntervention_Config": {
                              "Cost_To_Consumer": 1,
                              "Drug_Type": "FirstLineCombo",
                              "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT",
                              "Primary_Decay_Time_Constant": 180,
                              "Remaining_Doses": 1,
                              "Secondary_Decay_Time_Constant": 0,
                              "TB_Drug_Clearance_Rate": 0.2,
                              "TB_Drug_Inactivation_Rate": 0.000000001,
                              "class": "AntiTBDrug"
                         },
                         "Event_Or_Config": "Config",
                         "Tendency": 0.001,
                         "class": "HealthSeekingBehaviorUpdateable"
                    },
                    "Number_Repetitions": 1,
                    "Target_Demographic": "Everyone",
                    "class": "StandardInterventionDistributionEventCoordinator"
               },
               "Event_Name": "Start HSB",
               "Nodeset_Config": {
                    "class": "NodeSetAll"
               },
               "Start_Day": 20,
               "class": "CampaignEvent"
          },
          {
               "class": "CampaignEvent",
               "Event_Name": "Drugs after TB activation",
               "Nodeset_Config": {
                    "class": "NodeSetAll"
               },
               "Start_Day": 100,
               "Event_Coordinator_Config": {
                    "class": "StandardInterventionDistributionEventCoordinator",
                    "Number_Repetitions": 1,
                    "Target_Demographic": "Everyone",
                    "Demographic_Coverage": 1,
                    "Intervention_Config": {
                         "class": "HealthSeekingBehaviorUpdate",
                         "New_Tendency": 0.1
                    }
               }
          }
     ],
     "Use_Defaults": 1
}

IndividualImmunityChanger¶

The IndividualImmunityChanger intervention class acts essentially as a MultiEffectVaccine, with the exception of how the behavior is implemented. Rather than attaching a persistent vaccine intervention object to an individual’s intervention list (as a campaign-individual-multieffectboostervaccine does), the IndividualImmunityChanger directly alters the immune modifiers of the individual’s susceptibility object and is then immediately disposed of. Any immune waning is not governed by Waning effect classes, as MultiEffectVaccine is, but rather by the immunity waning parameters in the configuration file.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Maximum	Default	Description	Example
Boost_Acquire	float	1	0	Specifies the boosting effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.	{ "Boost_Acquire": 0.7 }
Boost_Mortality	float	1	0	Specifies the boosting effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.	{ "Boost_Mortality": 1.0 }
Boost_Threshold_Acquire	float	1	0	Specifies how much acquisition immunity is required before the vaccine changes from a prime to a boost.	{ "Boost_Threshold_Acquire": 0.0 }
Boost_Threshold_Mortality	float	1	0	Specifies how much mortality immunity is required before the vaccine changes from a prime to a boost.	{ "Boost_Threshold_Mortality": 0.0 }
Boost_Threshold_Transmit	float	1	0	Specifies how much transmission immunity is required before the vaccine changes from a prime to a boost.	{ "Boost_Threshold_Transmit": 0.0 }
Boost_Transmit	float	1	0	Specifies the boosting effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.	{ "Boost_Transmit": 0.5 }
Cost_To_Consumer	float	999999	10	The unit cost per vaccine (unamortized).	{ "Cost_To_Consumer": 10.0 }
Prime_Acquire	float	1	0	Specifies the priming effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.	{ "Prime_Acquire": 0.1 }
Prime_Mortality	float	1	0	Specifies the priming effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.	{ "Prime_Mortality": 0.3 }
Prime_Transmit	float	1	0	Specifies the priming effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.	{ "Prime_Transmit": 0.2 }

{
    "Use_Defaults": 1,
    "Campaign_Name": "Generic Seattle Regression Campaign",
    "Events": [{
        "Start_Day": 10,
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 1.0,
            "Intervention_Config": {
                "class": "IndividualImmunityChanger",
                "Cost_To_Consumer": 10.0,
                "Prime_Acquire": 0.1,
                "Prime_Transmit": 0.2,
                "Prime_Mortality": 0.3,
                "Boost_Acquire": 0.7,
                "Boost_Transmit": 0.7,
                "Boost_Mortality": 1.0,
                "Boost_Threshold_Acquire": 0.2,
                "Boost_Threshold_Transmit": 0.1,
                "Boost_Threshold_Mortality": 0.1
            }
        }
    }]
}

IVCalendar¶

The IVCalendar intervention class contains a list of ages when an individual will receive the actual intervention. In IVCalendar, there is a list of actual interventions where the distribution is dependent on whether the individual’s age matches the next date in the calendar. This implies that at a certain age, the list of actual interventions will be distributed according to a given probability. While a typical use case might involve the distribution of calendars by a BirthTriggeredIV in the context of a routine vaccination schedule, calendars may also be distributed directly to individuals.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Actual_IndividualIntervention_Configs	array of JSON objects	NA	NA	NA	An array of interventions that will be distributed as specified in the calendar. This parameter selects a class for the intervention and configures the parameters specific for that intervention class.	{ "Actual_IndividualIntervention_Config": { "class": "IVCalendar", "Actual_IndividualIntervention_Configs": [{ "class": "BCGVaccine", "Cost_To_Consumer": 10, "Vaccine_Take": 1, "Vaccine_Take_Age_Decay_Rate": 0, "Waning_Config": { "Initial_Effect": 1, "Box_Duration": 3650, "Decay_Rate_Factor": 3650, "class": "WaningEffectBoxExponential" } }] } }
Age	float	0	45625	NA	An array of ages (in days). In IVCalendar, there is a list of actual interventions where the distribution is dependent on whether the individual’s age matches the next date in the calendar.	{ "Calendar": [{ "Age": 60, "Probability": 1 }, { "Age": 120, "Probability": 1 }, { "Age": 180, "Probability": 1 }, { "Age": 510, "Probability": 0.9 }, { "Age": 1825, "Probability": 0.95 }, { "Age": 4200, "Probability": 0.95 } ] }
Calendar	array of JSON objects	NA	NA	NA	An array of ages, days and the probabilities of receiving the list of interventions at each age. Calendar has two parameters: Probability and Age.	{ "Calendar": [{ "Age": 60, "Probability": 1 }, { "Age": 120, "Probability": 1 }, { "Age": 180, "Probability": 1 }, { "Age": 510, "Probability": 0.9 }, { "Age": 1825, "Probability": 0.95 }, { "Age": 4200, "Probability": 0.95 } ] }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Dropout	boolean	NA	NA	0	If set to true (1), when an intervention distribution is missed, all subsequent interventions are also missed. If set to false (0), all calendar dates/doses are applied independently of each other.	{ "Actual_IndividualIntervention_Config": { "class": "IVCalendar", "Dont_Allow_Duplicates": 0, "Dropout": 0, "Calendar": [{ "Age": 12045, "Probability": 1 }] } }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Probability	float	0	1	NA	The probability of an individual receiving the list of actual interventions at the corresponding age.	{ "Calendar": [{ "Age": 60, "Probability": 1 }, { "Age": 120, "Probability": 1 }, { "Age": 180, "Probability": 1 }, { "Age": 510, "Probability": 0.9 }, { "Age": 1825, "Probability": 0.95 }, { "Age": 4200, "Probability": 0.95 } ] }

{
    "Use_Defaults": 1,
    "Campaign_Name": "BCG vaccination calendar distributed at birth",
    "Events": [{
        "Event_Name": "BCG vaccinations scheduled at birth",
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 1825,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Intervention_Config": {
                "class": "BirthTriggeredIV",
                "Demographic_Coverage": 0.9,
                "Actual_IndividualIntervention_Config": {
                    "class": "IVCalendar",
                    "Calendar": [{
                            "Age": 30,
                            "Probability": 1
                        },
                        {
                            "Age": 3650,
                            "Probability": 1
                        }
                    ],
                    "Dropout": 0,
                    "Actual_IndividualIntervention_Configs": [{
                        "class": "BCGVaccine",
                        "Cost_To_Consumer": 8,
                        "Vaccine_Take": 0.8,
                        "Vaccine_Take_Age_Decay_Rate": 0.2,
                        "Waning_Config": {
                            "Initial_Rate": 0.9,
                            "Decay_Time_Constant": 3650,
                            "class": "WaningEffectExponential"
                        }
                    }]
                }
            }
        }
    }]
}

MDRDiagnostic¶

The MDRDiagnostic intervention class is similar to SimpleDiagnostic and uses the same parameters as DiagnosticTreatNeg. However, MDRDiagnostic is used specifically for identifying individuals with multidrug-resistant TB (MDR-TB). Individuals who are not infected with MDR-TB receive a negative result.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Base_Sensitivity	float	0	1	1	The sensitivity of the diagnostic. This sets the proportion of the time that individuals with the condition being tested receive a positive diagnostic test. When set to 1, the diagnostic always accurately reflects the condition. When set to zero, then individuals who have the condition always receive a false-negative diagnostic test.	{ "Base_Sensitivity": 0.8 }
Base_Specificity	float	0	1	1	The specificity of the diagnostic. This sets the proportion of the time that individuals without the condition being tested receive a negative diagnostic test. When set to 1, the diagnostic always accurately reflects the lack of having the condition. When set to zero, then individuals who do not have the condition always receive a false-positive diagnostic test.	{ "Base_Specificity": 0.9 }
Cost_To_Consumer	float	0	3.40E+3	1	The unit ‘cost’ assigned to the diagnostic. Setting Cost_To_Consumer to zero for all other interventions, and to a non-zero amount for one intervention, provides a convenient way to track the number of times the intervention has been applied in a simulation.	{ "Cost_To_Consumer": 0.333 }
Days_To_Diagnosis	float	0	3.40E+3	0	The number of days from test until diagnosis.	{ "Days_To_Diagnosis": 0.0 }
Defaulters_Config	JSON object	NA	NA	NA	The intervention configuration given out when an individual did not return for test results when Event_Or_Config is set to ‘Config’.	{ "Defaulters_Config": { "class": "DelayedIntervention", "Delay_Distribution": "FIXED_DURATION", "Coverage": 1, "Delay_Period": 600, "Actual_IndividualIntervention_Configs": [{ "class": "AntiTBDrug", "Cost_To_Consumer": 90, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 180, "Remaining_Doses": 1, "TB_Drug_Clearance_Rate": 0.05, "TB_Drug_Inactivation_Rate": 0 }] } }
Defaulters_Event	enum	NA	NA	NoTrigger	Specifies an event that can trigger another intervention when the event occurs. Event_Or_Config must be set to ‘Event’, and an individual did not return for test results. See the Event list for possible values.	{ "Intervention_Config": { "class": "MDRDiagnostic", "Base_Sensitivity": 1, "Base_Specificity": 1, "Cost_To_Consumer": 10, "Days_To_Diagnosis": 14, "Treatment_Fraction": 1, "Event_Or_Config": "Event", "Positive_Diagnosis_Event": "TestPositiveOnSmear", "Negative_Diagnosis_Event": "NewSevereCase", "Defaulters_Event": "NewSevereCase" } }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Event_Or_Config	enum	NA	NA	Config	Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are: Event Config	{ "Event_Or_Config": "Config" }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
Negative_Diagnosis_Config	JSON object	NA	NA	NA	If Event_Or_Config is set to Config, this is the intervention given out when there is a negative diagnosis.	{ "Event_Or_Config": "Config", "Negative_Diagnosis_Config": { "class": "DelayedIntervention", "Delay_Distribution": "FIXED_DURATION", "Coverage": 1, "Delay_Period": 250 } }
Negative_Diagnosis_Event	enum	NA	NA	NoTrigger	If an individual tests negative, this specifies an event that may trigger another intervention when the event occurs. Only used when Event_Or_Config is set to Event. See Event list for possible values.	{ "Event_Or_Config": "Event", "Negative_Diagnosis_Event": "NewSevereCase" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Positive_Diagnosis_Config	JSON object	NA	NA	NA	The intervention distributed to individuals if they test positive. Only used when Event_Or_Config is set to Config.	{ "Positive_Diagnosis_Config": { "class": "MultiInterventionDistributor", "Intervention_List": [{ "Cost_To_Consumer": 0.333, "Secondary_Decay_Time_Constant": 1, "Vaccine_Take": 1, "Vaccine_Type": "AcquisitionBlocking", "class": "SimpleVaccine", "Waning_Config": { "Box_Duration": 3650, "Initial_Effect": 0.1, "class": "WaningEffectBox" } }] } }
Positive_Diagnosis_Event	enum	NA	NA	No_Trigger	If the test is positive, this specifies an event that can trigger another intervention when the event occurs. Only used if Event_Or_Config is set to Event. See Event list for possible values.	{ "Intervention_Config": { "Base_Sensitivity": 1.0, "Base_Specificity": 1.0, "Cost_To_Consumer": 0.0, "Days_To_Diagnosis": 0.0, "Event_Or_Config": "Event", "Positive_Diagnosis_Event": "TestedPositive_CureMeNow", "Treatment_Fraction": 1.0, "class": "SimpleDiagnostic" } }
Treatment_Fraction	float	0	1	1	The fraction of positive diagnoses that are treated.	{ "Intervention_Config": { "Base_Sensitivity": 1.0, "Base_Specificity": 1.0, "Cost_To_Consumer": 0.0, "Days_To_Diagnosis": 0.0, "Event_Or_Config": "Event", "Positive_Diagnosis_Event": "TestedPositive_CureMeNow", "Treatment_Fraction": 1.0, "class": "SimpleDiagnostic" } }
Treatment_Fraction_Negative_Diagnosis	float	0	1	1	The fraction of negative diagnoses that are treated.	{ "Treatment_Fraction_Negative_Diagnosis": 1.0 }

{
    "Use_Defaults": 1,
    "Events": [
        {
            "Event_Name": "Test-and-treat of MDR+ only",
            "class": "CampaignEvent",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 300,
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Demographic_Coverage": 1,
                "Include_Arrivals": 0,
                "Include_Departures": 0,
                "Number_Repetitions": 1,
                "Property_Restrictions": [],
                "Target_Demographic": "Everyone",
                "Timesteps_Between_Repetitions": -1,
                "Intervention_Config": {
                    "class": "MDRDiagnostic",
                    "Base_Sensitivity": 1,
                    "Base_Specificity": 1,
                    "Cost_To_Consumer": 10,
                    "Days_To_Diagnosis": 14,
                    "Treatment_Fraction": 1,
                    "Event_Or_Config": "Event",
                    "Positive_Diagnosis_Event": "TestPositiveOnSmear",
                    "Negative_Diagnosis_Event": "NewSevereCase",
                    "Defaulters_Event": "NewSevereCase"
                }
            }
        }
    ]
}

MigrateIndividuals¶

The MigrateIndividuals intervention class is used to force migration and is separate from the normal migration system. However, it does require that human migration is enabled.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Duration_At_Node_Distribution_Type	enum	NA	NA	NOT_INITIALIZED	The shape of the distribution for the amount of time spent at the destination node for intervention-based migration. Possible values are: NOT_INITIALIZED No distribution set. FIXED_DURATION A constant duration. UNIFORM_DURATION A uniform random draw for the duration. GAUSSIAN_DURATION Duration of the active period is defined by the mean and standard deviation of the Gaussian distribution. Negative values are truncated at zero. EXPONENTIAL_DURATION The active period is the mean of the exponential random draw. POISSON_DURATION The active period is the mean of the random Poisson draw. LOG_NORMAL_DURATION The active period is a log normal distribution defined by a mean and log width. BIMODAL_DURATION The distribution is bimodal, the duration is a fraction of the active period for a specified period of time and equal to the active period otherwise. PIECEWISE_CONSTANT The distribution is specified with a list of years and a matching list of values. The duration at a given year is that specified for the nearest previous year. PIECEWISE_LINEAR The distribution is specified with a list of years and matching list of values. The duration at a given year is a linear interpolation of the specified values. WEIBULL_DURATION The duration is a Weibull distribution with a given scale and shape. DUAL_TIMESCALE_DURATION The duration is two exponential distributions with given means.	{ "Duration_At_Node_Distribution_Type": "GAUSSIAN_DURATION" }
Duration_At_Node_Exponential_Period	float	0	3.40E+3	6	The period (1/rate) to use for an exponential distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to EXPONENTIAL_DURATION.	{ "Duration_At_Node_Distribution_Type": "EXPONENTIAL_DURATION", "Duration_At_Node_Exponential_Period": 4 }
Duration_At_Node_Fixed	float	0	3.40E+3	6	The value to use for a fixed distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to FIXED_DURATION.	{ "Duration_At_Node_Distribution_Type" : "FIXED_DURATION", "Duration_At_Node_Fixed": 10 }
Duration_At_Node_Gausian_Mean	float	0	3.40E+3	6	The mean value to use for a Gaussian distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to GAUSSIAN_DURATION.	{ "Duration_At_Node_Distribution_Type": "GAUSSIAN_DURATION", "Duration_At_Node_Gausian_Mean": 9.0, "Duration_At_Node_Gausian_StdDev": 2.0 }
Duration_At_Node_Gausian_StdDev	float	0	3.40E+3	1	The standard deviation to use for a Gaussian distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to GAUSSIAN_DURATION.	{ "Duration_At_Node_Distribution_Type": "GAUSSIAN_DURATION", "Duration_At_Node_Gausian_Mean": 9.0, "Duration_At_Node_Gausian_StdDev": 2.0 }
Duration_At_Node_Poisson_Mean	float	0	3.40E+3	6	The mean to use for a Poisson distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to POISSON_DURATION.	{ "Duration_At_Node_Distribution_Type": "POISSON_DURATION", "Duration_At_Node_Poisson_Mean": 4 }
Duration_At_Node_Uniform_Max	float	0	3.40E+3	0	The maximum value to use for a uniform distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to UNIFORM_DURATION.	{ "Duration_At_Node_Distribution_Type": "UNIFORM_DURATION", "Duration_At_Node_Uniform_Max": 75, "Duration_At_Node_Uniform_Min": 45 }
Duration_At_Node_Uniform_Min	float	0	3.40E+3	0	The minimum value to use for a uniform distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to UNIFORM_DURATION.	{ "Duration_At_Node_Distribution_Type": "UNIFORM_DURATION", "Duration_At_Node_Uniform_Max": 75, "Duration_At_Node_Uniform_Min": 45 }
Duration_Before_Leaving_Distribution_Type	enum	NA	NA	NOT_INITIALIZED	The shape of the distribution of the number of days to wait before migrating to the destination node for intervention-based migration. Possible values are: NOT_INITIALIZED No distribution set. FIXED_DURATION A constant duration. UNIFORM_DURATION A uniform random draw for the duration. GAUSSIAN_DURATION Duration of the active period is defined by the mean and standard deviation of the Gaussian distribution. Negative values are truncated at zero. EXPONENTIAL_DURATION The active period is the mean of the exponential random draw. POISSON_DURATION The active period is the mean of the random Poisson draw. LOG_NORMAL_DURATION The active period is a log normal distribution defined by a mean and log width. BIMODAL_DURATION The distribution is bimodal, the duration is a fraction of the active period for a specified period of time and equal to the active period otherwise. PIECEWISE_CONSTANT The distribution is specified with a list of years and a matching list of values. The duration at a given year is that specified for the nearest previous year. PIECEWISE_LINEAR The distribution is specified with a list of years and matching list of values. The duration at a given year is a linear interpolation of the specified values. WEIBULL_DURATION The duration is a Weibull distribution with a given scale and shape. DUAL_TIMESCALE_DURATION The duration is two exponential distributions with given means.	{ "Duration_Before_Leaving_Distribution_Type": "FIXED_DURATION" }
Duration_Before_Leaving_Exponential_Period	float	0	3.40E+3	6	The period (1/rate) to use for an exponential distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to EXPONENTIAL_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "EXPONENTIAL_DURATION", "Duration_Before_Leaving_Exponential_Period": 4 }
Duration_Before_Leaving_Fixed	float	0	3.40E+3	6	The value to use for a fixed distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to FIXED_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "FIXED_DURATION", "Duration_Before_Leaving_Fixed": 4 }
Duration_Before_Leaving_Gausian_Mean	float	0	3.40E+3	6	The mean value to use for a Gaussian distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to GAUSSIAN_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "GAUSSIAN_DURATION", "Duration_Before_Leaving_Gausian_Mean": 9.0, "Duration_Before_Leaving_Gausian_StdDev": 2.0 }
Duration_Before_Leaving_Gausian_StdDev	float	0	3.40E+3	1	The standard deviation to use for a Gaussian distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to GAUSSIAN_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "GAUSSIAN_DURATION", "Duration_Before_Leaving_Gausian_Mean": 9.0, "Duration_Before_Leaving_Gausian_StdDev": 2.0 }
Duration_Before_Leaving_Poisson_Mean	float	0	3.40E+3	6	The mean to use for a Poisson distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to POISSON_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "POISSON_DURATION", "Duration_Before_Leaving_Poisson_Mean": 3.0, }
Duration_Before_Leaving_Uniform_Max	float	0	3.40E+3	0	The maximum value to use for a uniform distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to UNIFORM_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "UNIFORM_DURATION", "Duration_Before_Leaving_Uniform_Max": 14, "Duration_Before_Leaving_Uniform_Min": 0, }
Duration_Before_Leaving_Uniform_Min	float	0	3.40E+3	0	The minimum value to use for a uniform distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to UNIFORM_DURATION.	{ "Duration_Before_Leaving_Distribution_Type": "UNIFORM_DURATION", "Duration_Before_Leaving_Uniform_Max": 14, "Duration_Before_Leaving_Uniform_Min": 0, }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
Is_Moving	boolean	NA	NA	0	Set to true (1) to indicate the individual is permanently moving to a new home node for intervention-based migration.	{ "Is_Moving": 1 }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
NodeID_To_Migrate_To	integer	0	2.15E+0	0	The destination node ID for intervention-based migration.	{ "NodeID_To_Migrate_To": 26 }

{
    "Use_Defaults": 1,
    "Events": [
        {
            "class": "CampaignEvent",
            "Start_Day": 5,
            "Nodeset_Config": {
                "class": "NodeSetNodeList",
                "Node_List": [ 1 ]
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Residents_Only": 1,
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1.0,
                "Intervention_Config": {
                    "class": "MigrateIndividuals",
                    "NodeID_To_Migrate_To": 2,
                    "Duration_Before_Leaving_Distribution_Type": "FIXED_DURATION",
                    "Duration_Before_Leaving_Fixed": 0,
                    "Duration_At_Node_Distribution_Type": "FIXED_DURATION",
                    "Duration_At_Node_Fixed": 999,
                    "Is_Moving": 0
                }
            }
        }
    ]
}

MultiEffectBoosterVaccine¶

The MultiEffectBoosterVaccine intervention class is derived from MultiEffectVaccine and preserves many of the same parameters. Upon distribution and successful take, the vaccine’s effect in each immunity compartment (acquisition, transmission, and mortality) is determined by the recipient’s immune state. If the recipient’s immunity modifier in the corresponding compartment is above a user-specified threshold, then the vaccine’s initial effect will be equal to the corresponding priming parameter. If the recipient’s immune modifier is below this threshold, then the vaccine’s initial effect will be equal to the corresponding boost parameter. After distribution, the effect wanes, just like a MultiEffectVaccine. The behavior is intended to mimic biological priming and boosting.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Acquire_Config	JSON object	NA	NA	NA	The configuration for multi-effect vaccine acquisition. Specify how this effect decays over time using one of the Waning effect classes.	{ "Acquire_Config": { "Initial_Effect": 0.9, "Decay_Time_Constant": 2525, "class": "WaningEffectExponential" } }
Boost_Acquire	float	0	1	0	Specifies the boosting effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.	{ "Boost_Acquire": 0.7 }
Boost_Mortality	float	0	1	0	Specifies the boosting effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.	{ "Boost_Mortality": 1.0 }
Boost_Threshold_Acquire	float	0	1	0	Specifies how much acquisition immunity is required before the vaccine changes from a prime to a boost.	{ "Boost_Threshold_Acquire": 0.0 }
Boost_Threshold_Mortality	float	0	1	0	Specifies how much mortality immunity is required before the vaccine changes from a prime to a boost.	{ "Boost_Threshold_Mortality": 0.0 }
Boost_Threshold_Transmit	float	0	1	0	Specifies how much transmission immunity is required before the vaccine changes from a prime to a boost.	{ "Boost_Threshold_Transmit": 0.0 }
Boost_Transmit	float	0	1	0	Specifies the boosting effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.	{ "Boost_Transmit": 0.5 }
Cost_To_Consumer	float	0	999999	10	The unit cost per vaccine (unamortized).	{ "Cost_To_Consumer": 10.0 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
Mortality_Config	JSON object	NA	NA	NA	The configuration for multi-effect vaccine mortality. Specify how this effect decays over time using one of the Waning effect classes.	{ "Mortality_Config": { "Initial_Effect": 1.0, "Decay_Time_Constant": 2525, "class": "WaningEffectExponential" } }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Prime_Acquire	float	0	1	0	Specifies the priming effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.	{ "Prime_Acquire": 0.1 }
Prime_Mortality	float	0	1	0	Specifies the priming effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.	{ "Prime_Mortality": 0.3 }
Prime_Transmit	float	0	1	0	Specifies the priming effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.	{ "Prime_Transmit": 0.2 }
Transmit_Config	JSON object	NA	NA	NA	The configuration for multi-effect vaccine transmission. Specify how this effect decays over time using one of the Waning effect classes.	{ "Transmit_Config": { "Initial_Effect": 0.6, "Decay_Time_Constant": 2525, "class": "WaningEffectExponential" } }
Vaccine_Take	float	0	1	1	The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }

{
    "Use_Defaults": 1,
    "Campaign_Name": "Generic Seattle Regression Campaign",
    "Events": [{
        "Event_Coordinator_Config": {
            "Demographic_Coverage": 1.0,
            "Intervention_Config": {
                "Cost_To_Consumer": 10.0,
                "Vaccine_Take": 1,
                "Prime_Acquire": 0.1,
                "Prime_Transmit": 0.2,
                "Prime_Mortality": 0.3,
                "Boost_Acquire": 0.7,
                "Boost_Transmit": 0.5,
                "Boost_Mortality": 1.0,
                "Boost_Threshold_Acquire": 0.0,
                "Boost_Threshold_Transmit": 0.0,
                "Boost_Threshold_Mortality": 0.0,
                "Acquire_Config": {
                    "Box_Duration": 100,
                    "Initial_Effect": 0.5,
                    "class": "WaningEffectBox"
                },
                "Transmit_Config": {
                    "Box_Duration": 100,
                    "Initial_Effect": 0.5,
                    "class": "WaningEffectBox"
                },
                "Mortality_Config": {
                    "Box_Duration": 100,
                    "Initial_Effect": 0.5,
                    "class": "WaningEffectBox"
                },
                "class": "MultiEffectBoosterVaccine"
            },
            "Target_Demographic": "Everyone",
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 1,
        "class": "CampaignEvent"
    }]
}

MultiEffectVaccine¶

The MultiEffectVaccine intervention class implements vaccine campaigns in the simulation. Vaccines can effect all of the following:

Reduce the likelihood of acquiring an infection
Reduce the likelihood of transmitting an infection
Reduce the likelihood of death

After distribution, the effect wanes over time.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Acquire_Config	JSON object	NA	NA	NA	The configuration for multi-effect vaccine acquisition. Specify how this effect decays over time using one of the Waning effect classes.	{ "Acquire_Config": { "Initial_Effect": 0.9, "Decay_Time_Constant": 2525, "class": "WaningEffectExponential" } }
Cost_To_Consumer	float	0	999999	10	The unit cost per vaccine (unamortized).	{ "Cost_To_Consumer": 10.0 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
Mortality_Config	JSON object	NA	NA	NA	The configuration for multi-effect vaccine mortality. Specify how this effect decays over time using one of the Waning effect classes.	{ "Mortality_Config": { "Initial_Effect": 1.0, "Decay_Time_Constant": 2525, "class": "WaningEffectExponential" } }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Transmit_Config	JSON object	NA	NA	NA	The configuration for multi-effect vaccine transmission. Specify how this effect decays over time using one of the Waning effect classes.	{ "Transmit_Config": { "Initial_Effect": 0.6, "Decay_Time_Constant": 2525, "class": "WaningEffectExponential" } }
Vaccine_Take	float	0	1	1	The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }

{
    "Events": [{
        "Event_Coordinator_Config": {
            "Demographic_Coverage": 1,
            "Intervention_Config": {
                "Cost_To_Consumer": 20,
                "Vaccine_Take": 1,
                "Vaccine_Type": "Generic",
                "class": "MultiEffectVaccine",
                "Acquire_Config": {
                    "Initial_Effect": 0.9,
                    "Decay_Time_Constant": 7300,
                    "class": "WaningEffectExponential"
                },
                "Transmit_Config": {
                    "Initial_Effect": 0.9,
                    "Decay_Time_Constant": 7300,
                    "class": "WaningEffectExponential"
                },
                "Mortality_Config": {
                    "Initial_Effect": 1.0,
                    "Decay_Time_Constant": 7300,
                    "class": "WaningEffectExponential"
                }
            },
            "Property_Restrictions": [
                "Accessibility:VaccineTake"
            ],
            "Target_Age_Max": 100,
            "Target_Age_Min": 12,
            "Target_Demographic": "ExplicitAgeRanges",
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 1,
        "class": "CampaignEvent"
    }],
    "Use_Defaults": 1
}

MultiInterventionDistributor¶

The MultiInterventionDistributer intervention class allows you to input a list of interventions, rather than just a single intervention, to be distributed simultaneously to the same individuals.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Intervention_List	array of JSON objects	NA	NA	NA	The list of individual interventions that is distributed by MultiInterventionDistributor.	{ "Actual_IndividualIntervention_Config": { "Intervention_List": [{ "Cost_To_Consumer": 1, "Dosing_Type": "FullTreatmentNewDetectionTech", "Drug_Type": "Artemether", "class": "AntimalarialDrug" }, { "Cost_To_Consumer": 1, "Dosing_Type": "FullTreatmentNewDetectionTech", "Drug_Type": "Lumefantrine", "class": "AntimalarialDrug" } ], "class": "MultiInterventionDistributor" } }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Number_Repetitions	integer	-1	1000	1	The number of times an intervention is given, used with Timesteps_Between_Repetitions.	{ "Event_Coordinator_Config": { "Intervention_Config": { "class": "Outbreak", "Num_Cases": 1 }, "Number_Repetitions": 10, "Timesteps_Between_Repetitions": 50, "class": "StandardInterventionDistributionEventCoordinator" } }
Timesteps_Between_Repetitions	integer	-1	10000	-1	The repetition interval.	{ "Timesteps_Between_Repetitions": 50 }

{
  "Events": [{
    "class": "CampaignEvent",
    "Nodeset_Config": {
      "class": "NodeSetAll"
    },
    "Start_Day": 0,
    "Event_Coordinator_Config": {
      "class": "CommunityHealthWorkerEventCoordinator",
      "Demographic_Coverage": 1,
      "Target_Demographic": "Everyone",
      "Target_Residents_Only": 0,
      "Trigger_Condition_List": [
        "HappyBirthday"
      ],
      "Amount_In_Shipment": 1000,
      "Days_Between_Shipments": 30,
      "Max_Distributed_Per_Day": 50,
      "Duration": 700,
      "Waiting_Period": 7,
      "Max_Stock": 1000,
      "Initial_Amount": 500,
      "Initial_Amount_Distribution_Type": "FIXED_DURATION",
      "Intervention_Config": {
        "class": "MultiInterventionDistributor",
        "Intervention_List": [{
            "class": "SimpleVaccine",
            "Cost_To_Consumer": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Vaccine_Take": 1,
            "Efficacy_Is_Multiplicative": 1,
            "Waning_Config": {
              "class": "WaningEffectConstant",
              "Initial_Effect": 0.9
            }
          },
          {
            "class": "BroadcastEvent",
            "Broadcast_Event": "Received_Treatment"
          }
        ]
      }
    }
  }],
  "Use_Defaults": 1
}

OutbreakIndividual¶

The OutbreakIndividual intervention class introduces contagious diseases that are compatible with the simulation type to existing individuals using the individual targeted features configured in the appropriate event coordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

Warning

The event containing the outbreak “intervention” must be the last one listed in the campaign file or none of the disease control interventions will take place.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Antigen	integer	0	10	0	The antigenic ID of the outbreak infection.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease", "class": "OutbreakIndividual" } }
Genome	integer	-2.15E+0	2.15E+0	0	The genetic ID of the outbreak infection.	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "Outbreak_Source": "PrevalenceIncrease", "class": "OutbreakIndividual", "Incubation_Period_Override": 0 } }
Ignore_Immunity	boolean	NA	NA	1	Individuals will be force-infected (with a specific strain) regardless of actual immunity level when set to true (1).	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "class": "OutbreakIndividual", "Ignore_Immunity": 0 } }
Ignore_Immunity	boolean	NA	NA	1	Individuals will be force-infected (with a specific strain) regardless of actual immunity level when set to true (1).	{ "Intervention_Config": { "Antigen": 0, "Genome": 0, "class": "OutbreakIndividual", "Ignore_Immunity": 0 } }
Incubation_Period_Override	integer	-1	2.15E+0	-1	The incubation period, in days, that infected individuals will go through before becoming infectious. This value overrides the incubation period set in the configuration file. Set to -1 to honor the configuration parameter settings.	{ "Incubation_Period_Override": 0 }

{
    "Events": [{
        "Event_Coordinator_Config": {
            "Demographic_Coverage": 0.005,
            "Intervention_Config": {
                "Outbreak_Source": "PrevalenceIncrease",
                "class": "OutbreakIndividual"
            },
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Event_Name": "Outbreak",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 1,
        "class": "CampaignEvent"
    }],
    "Use_Defaults": 1
}

PropertyValueChanger¶

The PropertyValueChanger intervention class assigns new individual property values to individuals. You must update one property value and have the option to update another using New_Property_Value. This parameter is generally used to move patients from one intervention state in the health care cascade (InterventionStatus) to another, though it can be used for any individual property. Individual property values are user-defined in the demographics file (see NodeProperties and IndividualProperties for more information). Note that the HINT feature does not need to be enabled to use this intervention. To instead change node properties, use NodePropertyValueChanger.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Daily_Probability	float	0	1	1	The probability that an individual will move to the Target_Property_Value.	{ "Intervention_Config": { "class": "PropertyValueChanger", "Disqualifying_Properties": [], "New_Property_Value": "", "Target_Property_Key": "Risk", "Target_Property_Value": "LOW", "Daily_Probability": 1.0, "Maximum_Duration": 0, "Revert": 0 } }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
Maximum_Duration	float	-1	3.40E+3	3.40E+38	The maximum amount of time individuals have to move to a new group. This timing works in conjunction with Daily_Probability.	{ "Intervention_Config": { "class": "PropertyValueChanger", "Disqualifying_Properties": [], "New_Property_Value": "", "Target_Property_Key": "Risk", "Target_Property_Value": "LOW", "Daily_Probability": 1.0, "Maximum_Duration": 0, "Revert": 0 } }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Revert	float	0	10000	0	The number of days before an individual moves back to their original group.	{ "Intervention_Config": { "class": "PropertyValueChanger", "Disqualifying_Properties": [], "New_Property_Value": "", "Target_Property_Key": "Risk", "Target_Property_Value": "LOW", "Daily_Probability": 1.0, "Maximum_Duration": 0, "Revert": 0 } }
Target_Property_Key	string	NA	NA	NA	The name of the individual property type whose value will be updated by the intervention.	{ "Intervention_Config": { "class": "PropertyValueChanger", "Disqualifying_Properties": [], "New_Property_Value": "", "Target_Property_Key": "Risk", "Target_Property_Value": "LOW", "Daily_Probability": 1.0, "Maximum_Duration": 0, "Revert": 0 } }
Target_Property_Value	string	NA	NA	NA	The user-defined value of the individual property that will be assigned to the individual.	{ "Intervention_Config": { "class": "PropertyValueChanger", "Disqualifying_Properties": [], "Target_Property_Key": "Risk", "Target_Property_Value": "LOW", "New_Property_Value": "", "Daily_Probability": 1.0, "Maximum_Duration": 0, "Revert": 0 } }

{
    "Events": [
        {
            "class": "CampaignEvent",
            "Start_Day": 10,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1.0,
                "Property_Restrictions": [
                    "Risk:LOW"
                ],
                "Intervention_Config": {
                    "class": "PropertyValueChanger",
                    "Disqualifying_Properties": [ "InterventionStatus:Diagnosed"],
                    "New_Property_Value": "InterventionStatus:Monitor",
                    "Target_Property_Key" : "Risk",
                    "Target_Property_Value" : "HIGH",
                    "Daily_Probability" : 1.0,
                    "Maximum_Duration" : 0,
                    "Revert" : 10
                }
            }
        }
    ],
    "Use_Defaults": 1
}

SimpleBoosterVaccine¶

The SimpleBoosterVaccine intervention class is derived from SimpleVaccine and preserves many of the same parameters. The behavior is much like SimpleVaccine, except that upon distribution and successful take, the vaccine’s effect is determined by the recipient’s immune state. If the recipient’s immunity modifier in the corresponding channel (acquisition, transmission, or mortality) is above a user-specified threshold, then the vaccine’s initial effect will be equal to the corresponding priming parameter. If the recipient’s immune modifier is below this threshold, then the vaccine’s initial effect will be equal to the corresponding boosting parameter. After distribution, the effect wanes, just like SimpleVaccine. In essence, this intervention provides a SimpleVaccine intervention with one effect to all naive (below- threshold) individuals, and another effect to all primed (above-threshold) individuals; this behavior is intended to mimic biological priming and boosting.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Boost_Effect	float	0	1	1	Specifies the boosting effect on [acquisition/transmission/mortality] immunity for previously exposed individuals (either natural or vaccine-derived). This does not replace current immunity, it builds multiplicatively on top of it.	{ "Intervention_Config": { "Cost_To_Consumer": 10.0, "Vaccine_Take": 1, "Vaccine_Type": "MortalityBlocking", "Prime_Effect": 0.25, "Boost_Effect": 0.45, "Boost_Threshold": 0.0, "Waning_Config": { "Box_Duration": 10, "Initial_Effect": 1, "class": "WaningEffectBox" }, "class": "SimpleBoosterVaccine" } }
Boost_Threshold	float	0	1	0	Specifies how much immunity is required before the vaccine changes from a priming effect to a boosting effect.	{ "Intervention_Config": { "Cost_To_Consumer": 10.0, "Vaccine_Take": 1, "Vaccine_Type": "MortalityBlocking", "Prime_Effect": 0.25, "Boost_Effect": 0.45, "Boost_Threshold": 0.0, "Waning_Config": { "Box_Duration": 10, "Initial_Effect": 1, "class": "WaningEffectBox" }, "class": "SimpleBoosterVaccine" } }
Cost_To_Consumer	float	0	999999	10	The unit cost per vaccine (unamortized).	{ "Cost_To_Consumer": 10.0 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Efficacy_Is_Multiplicative	boolean	NA	NA	1	The overall vaccine efficacy when individuals receive more than one vaccine. When set to true (1), the vaccine efficacies are multiplied together; when set to false (0), the efficacies are additive.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Prime_Effect	float	0	1	1	Specifies the priming effect on [acquisition/transmission/mortality] immunity for naive individuals (without natural or vaccine-derived immunity).	{ "Intervention_Config": { "Cost_To_Consumer": 10.0, "Vaccine_Take": 1, "Vaccine_Type": "MortalityBlocking", "Prime_Effect": 0.25, "Boost_Effect": 0.45, "Boost_Threshold": 0.0, "Waning_Config": { "Box_Duration": 10, "Initial_Effect": 1, "class": "WaningEffectBox" }, "class": "SimpleBoosterVaccine" } }
Vaccine_Take	float	0	1	1	The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Vaccine_Type	enum	NA	NA	Generic	The type of vaccine to distribute in a vaccine intervention. Possible values are: Generic The vaccine can reduce transmission, acquisition, and mortality. TransmissionBlocking The vaccine will reduce pathogen transmission. AcquisitionBlocking The vaccine will reduce the acquisition of the pathogen by reducing the force of infection experienced by the vaccinated individual. MortalityBlocking The vaccine reduces the disease-mortality rate of a vaccinated individual.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Waning_Config	JSON object	NA	NA	NA	The configuration of how the intervention efficacy wanes over time. Specify how this effect decays over time using one of the Waning effect classes.	{ "Waning_Config": { "Box_Duration": 3650, "Initial_Effect": 1, "class": "WaningEffectBox" } }

{
    "Use_Defaults": 1,
    "Campaign_Name": "Generic Seattle Regression Campaign",
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 20,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 1.0,
            "Intervention_Config": {
                "class": "SimpleBoosterVaccine",
                "Cost_To_Consumer": 10.0,
                "Vaccine_Take": 1,
                "Vaccine_Type": "MortalityBlocking",
                "Prime_Effect": 0.25,
                "Boost_Effect": 0.45,
                "Boost_Threshold": 0.0,
                "Waning_Config": {
                    "Box_Duration": 10,
                    "Initial_Effect": 1,
                    "class": "WaningEffectBox"
                }
            }
        }
    }]
}

SimpleDiagnostic¶

The SimpleDiagnostic intervention class identifies infected individuals, regardless of disease state, based on specified diagnostic sensitivity and specificity. Diagnostics are a key component of modern disease control efforts, whether used to identify high-risk individuals, infected individuals, or drug resistance. This intervention class distributes a specified intervention to a fraction of individuals who test positive.

To specify additional outcomes for individuals who test negative or who default (in other words, who do not receive their test result), use DiagnosticTreatNeg instead.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Cost_To_Consumer	float	0	3.40E+3	1	The unit ‘cost’ assigned to the diagnostic. Setting Cost_To_Consumer to zero for all other interventions, and to a non-zero amount for one intervention, provides a convenient way to track the number of times the intervention has been applied in a simulation.	{ "Cost_To_Consumer": 0.333 }
Days_To_Diagnosis	float	0	3.40E+3	0	The number of days from test until diagnosis.	{ "Days_To_Diagnosis": 0.0 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Positive_Diagnosis_Config	JSON object	NA	NA	NA	The intervention distributed to individuals if they test positive. Only used when Event_Or_Config is set to Config.	{ "Positive_Diagnosis_Config": { "class": "MultiInterventionDistributor", "Intervention_List": [{ "Cost_To_Consumer": 0.333, "Secondary_Decay_Time_Constant": 1, "Vaccine_Take": 1, "Vaccine_Type": "AcquisitionBlocking", "class": "SimpleVaccine", "Waning_Config": { "Box_Duration": 3650, "Initial_Effect": 0.1, "class": "WaningEffectBox" } }] } }
Positive_Diagnosis_Event	enum	NA	NA	No_Trigger	If the test is positive, this specifies an event that can trigger another intervention when the event occurs. Only used if Event_Or_Config is set to Event. See Event list for possible values.	{ "Intervention_Config": { "Base_Sensitivity": 1.0, "Base_Specificity": 1.0, "Cost_To_Consumer": 0.0, "Days_To_Diagnosis": 0.0, "Event_Or_Config": "Event", "Positive_Diagnosis_Event": "TestedPositive_CureMeNow", "Treatment_Fraction": 1.0, "class": "SimpleDiagnostic" } }
Treatment_Fraction	float	0	1	1	The fraction of positive diagnoses that are treated.	{ "Intervention_Config": { "Base_Sensitivity": 1.0, "Base_Specificity": 1.0, "Cost_To_Consumer": 0.0, "Days_To_Diagnosis": 0.0, "Event_Or_Config": "Event", "Positive_Diagnosis_Event": "TestedPositive_CureMeNow", "Treatment_Fraction": 1.0, "class": "SimpleDiagnostic" } }

{
    "Events":[{
            "class":"CampaignEvent",
            "Start_Day":200,
            "Nodeset_Config":{
                "class":"NodeSetAll"
            },
            "Event_Coordinator_Config":{
                "class":"StandardInterventionDistributionEventCoordinator",
                "Target_Demographic":"Everyone",
                "Demographic_Coverage": 1.0,
                "Intervention_Config":{
                    "class":"SimpleDiagnostic",
                    "Base_Sensitivity":1.0,
                    "Base_Specificity":1.0,
                    "Cost_To_Consumer":0,
                    "Days_To_Diagnosis":5.0,
                    "Dont_Allow_Duplicates":0,
                    "Event_Or_Config":"Event",
                    "Positive_Diagnosis_Event":"Acorn",
                    "Intervention_Name":"Diagnostic_Sample",
                    "Treatment_Fraction":1.0
                }
            }
        }],
    "Use_Defaults":1
}

SimpleHealthSeekingBehavior¶

The SimpleHealthSeekingBehavior intervention class models the time delay that typically occurs between when an individual experiences onset of symptoms and when they seek help from a health care provider. Several factors may contribute to such delays including accessibility, cost, and trust in the health care system. This intervention models this time delay as an exponential process; at every time step, the model draws randomly to determine if the individual will receive the specified intervention. As an example, this intervention can be nested in a NodeLevelHealthTriggeredIV so that when an individual is infected, he or she receives a SimpleHealthSeekingBehavior, representing that the individual will now seek care. The individual subsequently seeks care with an exponentially distributed delay and ultimately receives the specified intervention.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Actual_IndividualIntervention_Config	JSON object	NA	NA	NA	The configuration of an actual intervention sought. Selects a class for the intervention and configures the parameters specific for that intervention class.	{ "Actual_IndividualIntervention_Config": { "Cost_To_Consumer": 1, "Drug_Type": "FirstLineCombo", "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT", "Primary_Decay_Time_Constant": 180, "Remaining_Doses": 1, "Secondary_Decay_Time_Constant": 0, "TB_Drug_Clearance_Rate": 0.2, "TB_Drug_Inactivation_Rate": 0.000000001, "class": "AntiTBDrug" } }
Actual_IndividualIntervention_Event	enum	NA	NA	NoTrigger	The event of an actual intervention sought. Selects a class for the intervention and configures the parameters specific for that intervention class. See Event list for possible values.	{ "Actual_IndividualIntervention_Config": { "Actual_IndividualIntervention_Event": "ProviderOrdersTBTest", "Tendency": 0.005, "Event_Or_Config": "Event", "class": "SimpleHealthSeekingBehavior" } }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Event_Or_Config	enum	NA	NA	Config	Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are: Event Config	{ "Event_Or_Config": "Config" }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Single_Use	boolean	NA	NA	1	If set to true (1), the health-seeking behavior gets used once and discarded. If set to false (0), it remains indefinitely.	{ "Single_Use": 1 }
Tendency	float	0	1	1	The probability of seeking healthcare.	{ "Tendency": 0.01 }

{
     "Use_Defaults": 1,
     "Events": [{
          "class": "CampaignEvent",
          "Event_Name": "Drugs after TB activation",
          "Nodeset_Config": {
               "class": "NodeSetAll"
          },
          "Start_Day": 9125,
          "Event_Coordinator_Config": {
               "class": "StandardInterventionDistributionEventCoordinator",
               "Number_Repetitions": 1,
               "Target_Demographic": "Everyone",
               "Demographic_Coverage": 1,
               "Intervention_Config": {
                    "class": "NodeLevelHealthTriggeredIV",
                    "Trigger_Condition_List": ["TBActivation"],
                    "Actual_IndividualIntervention_Config": {
                         "class": "SimpleHealthSeekingBehavior",
                         "Event_Or_Config": "Config",
                         "Tendency": 0.0015,
                         "Actual_IndividualIntervention_Config": {
                              "class": "AntiTBDrug",
                              "Cost_To_Consumer": 90,
                              "Drug_Type": "FirstLineCombo",
                              "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT",
                              "Primary_Decay_Time_Constant": 180,
                              "Remaining_Doses": 1,
                              "Secondary_Decay_Time_Constant": 0,
                              "TB_Drug_Clearance_Rate": 0.02,
                              "TB_Drug_Inactivation_Rate": 0.3
                         }
                    }
               }
          }
     }]
}

SimpleVaccine¶

The SimpleVaccine intervention class implements vaccine campaigns in the simulation. Vaccines can have an effect on one of the following:

Reduce the likelihood of acquiring an infection
Reduce the likelihood of transmitting an infection
Reduce the likelihood of death

To configure vaccines that have an effect on more than one of these, use MultiEffectVaccine instead.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Cost_To_Consumer	float	0	999999	10	The unit cost per vaccine (unamortized).	{ "Cost_To_Consumer": 10.0 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Efficacy_Is_Multiplicative	boolean	NA	NA	1	The overall vaccine efficacy when individuals receive more than one vaccine. When set to true (1), the vaccine efficacies are multiplied together; when set to false (0), the efficacies are additive.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Event_Or_Config	enum	NA	NA	Config	Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are: Event Config	{ "Event_Or_Config": "Config" }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Vaccine_Take	float	0	1	1	The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Vaccine_Type	enum	NA	NA	Generic	The type of vaccine to distribute in a vaccine intervention. Possible values are: Generic The vaccine can reduce transmission, acquisition, and mortality. TransmissionBlocking The vaccine will reduce pathogen transmission. AcquisitionBlocking The vaccine will reduce the acquisition of the pathogen by reducing the force of infection experienced by the vaccinated individual. MortalityBlocking The vaccine reduces the disease-mortality rate of a vaccinated individual.	{ "Intervention_Config": { "class": "SimpleVaccine", "Cost_To_Consumer": 10, "Vaccine_Type": "AcquisitionBlocking", "Vaccine_Take": 1, "Efficacy_Is_Multiplicative": 0, "Waning_Config": { "class": "WaningEffectConstant", "Initial_Effect": 0.3 } } }
Waning_Config	JSON object	NA	NA	NA	The configuration of how the intervention efficacy wanes over time. Specify how this effect decays over time using one of the Waning effect classes.	{ "Waning_Config": { "Box_Duration": 3650, "Initial_Effect": 1, "class": "WaningEffectBox" } }

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 60,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 0.5,
            "Intervention_Config": {
                "class": "SimpleVaccine",
                "Cost_To_Consumer": 10.0,
                "Reduced_Transmit": 0,
                "Vaccine_Take": 1,
                "Vaccine_Type": "AcquisitionBlocking",
                "Waning_Config": {
                    "Box_Duration": 3650,
                    "Initial_Effect": 1,
                    "class": "WaningEffectBox"
                }
            }
        }
    }],
    "Use_Defaults": 1
}

SmearDiagnostic¶

The SmearDiagnostic intervention class identifies individuals who have active, smear-positive TB. This intervention is similar to SimpleDiagnostic, which identifies infected individuals (regardless of disease state) and ActiveDiagnostic, which identifies individuals who have active TB (regardless of disease presentation). If a smear-positive diagnosis is returned, an actual intervention is distributed, or an event is specified that may trigger another intervention when the event occurs.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Base_Sensitivity	float	0	1	1	The sensitivity of the diagnostic. This sets the proportion of the time that individuals with the condition being tested receive a positive diagnostic test. When set to 1, the diagnostic always accurately reflects the condition. When set to zero, then individuals who have the condition always receive a false-negative diagnostic test.	{ "Base_Sensitivity": 0.8 }
Base_Specificity	float	0	1	1	The specificity of the diagnostic. This sets the proportion of the time that individuals without the condition being tested receive a negative diagnostic test. When set to 1, the diagnostic always accurately reflects the lack of having the condition. When set to zero, then individuals who do not have the condition always receive a false-positive diagnostic test.	{ "Base_Specificity": 0.9 }
Cost_To_Consumer	float	0	3.40E+3	1	The unit ‘cost’ assigned to the diagnostic. Setting Cost_To_Consumer to zero for all other interventions, and to a non-zero amount for one intervention, provides a convenient way to track the number of times the intervention has been applied in a simulation.	{ "Cost_To_Consumer": 0.333 }
Days_To_Diagnosis	float	0	3.40E+3	0	The number of days from test until diagnosis.	{ "Days_To_Diagnosis": 0.0 }
Disqualifying_Properties	string	NA	NA	NA	A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.	{ "Disqualifying_Properties": [ "InterventionStatus:LostForever" ] }
Dont_Allow_Duplicates	boolean	NA	NA	0	If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.	{ "Dont_Allow_Duplicates": 0 }
Event_Or_Config	enum	NA	NA	Config	Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are: Event Config	{ "Event_Or_Config": "Config" }
Intervention_Name	string	NA	NA	NA	The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.	{ "Intervention_Name":"Diagnostic_Sample" }
New_Property_Value	string	NA	NA	NA	An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.	{ "New_Property_Value": "InterventionStatus:None" }
Positive_Diagnosis_Config	JSON object	NA	NA	NA	The intervention distributed to individuals if they test positive. Only used when Event_Or_Config is set to Config.	{ "Positive_Diagnosis_Config": { "class": "MultiInterventionDistributor", "Intervention_List": [{ "Cost_To_Consumer": 0.333, "Secondary_Decay_Time_Constant": 1, "Vaccine_Take": 1, "Vaccine_Type": "AcquisitionBlocking", "class": "SimpleVaccine", "Waning_Config": { "Box_Duration": 3650, "Initial_Effect": 0.1, "class": "WaningEffectBox" } }] } }
Positive_Diagnosis_Event	enum	NA	NA	No_Trigger	If the test is positive, this specifies an event that can trigger another intervention when the event occurs. Only used if Event_Or_Config is set to Event. See Event list for possible values.	{ "Intervention_Config": { "Base_Sensitivity": 1.0, "Base_Specificity": 1.0, "Cost_To_Consumer": 0.0, "Days_To_Diagnosis": 0.0, "Event_Or_Config": "Event", "Positive_Diagnosis_Event": "TestedPositive_CureMeNow", "Treatment_Fraction": 1.0, "class": "SimpleDiagnostic" } }
Treatment_Fraction	float	0	1	1	The fraction of positive diagnoses that are treated.	{ "Intervention_Config": { "Base_Sensitivity": 1.0, "Base_Specificity": 1.0, "Cost_To_Consumer": 0.0, "Days_To_Diagnosis": 0.0, "Event_Or_Config": "Event", "Positive_Diagnosis_Event": "TestedPositive_CureMeNow", "Treatment_Fraction": 1.0, "class": "SimpleDiagnostic" } }

{
    "Use_Defaults": 1,
    "Events": [
        {
            "Event_Name": "Test-and-treat of smear+ only",
            "class": "CampaignEvent",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 110,
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Demographic_Coverage": 1,
                "Include_Arrivals": 0,
                "Include_Departures": 0,
                "Number_Repetitions": -1,
                "Property_Restrictions": [],
                "Target_Demographic": "Everyone",
                "Timesteps_Between_Repetitions": 30,
                "Intervention_Config": {
                    "class": "SmearDiagnostic",
                    "Base_Sensitivity": 1,
                    "Base_Specificity": 1,
                    "Cost_To_Consumer": 10,
                    "Days_To_Diagnosis": 0,
                    "Treatment_Fraction": 1,
                    "Event_Or_Config": "Event",
                    "Positive_Diagnosis_Event": "TestPositiveOnSmear"
                }
            }
        }
    ]
}

Waning effect classes¶

The following classes are nested within interventions (both individual-level and node-level) to indicate how their efficacy wanes over time. They can be used with several parameters including Blocking_Config, Killing_Config, and Waning_Config.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.

See the example below that uses a mix of different waning effect classes and the tables below that describe all parameters that can be used with each waning effect class.

{
    "Events": [
        {
            "class": "CampaignEvent",
            "Start_Day": 1,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1.0,
                "Number_Repetitions": -1,
                "Timesteps_Between_Repetitions": 60,
                "Intervention_Config": {
                    "class": "SimpleBednet",
                    "Cost_To_Consumer": 5,
                    "Usage_Config": {
                        "class": "WaningEffectRandomBox",
                        "Initial_Effect": 1.0,
                        "Expected_Discard_Time" : 60
                    },
                    "Blocking_Config": {
                        "Box_Duration": 100,
                        "Decay_Time_Constant": 150,
                        "Initial_Effect": 0.5
                    },
                    "Killing_Config": {
                        "class": "WaningEffectConstant",
                        "Initial_Effect": 1.0
                    }
                }
            }
        }
    ],
    "Use_Defaults": 1
}

Contents

WaningEffectBox
WaningEffectBoxExponential
WaningEffectConstant
WaningEffectExponential
WaningEffectMapLinear
WaningEffectMapLinearAge
WaningEffectMapLinearSeasonal
WaningEffectMapPiecewise
WaningEffectRandomBox

WaningEffectBox ¶

The efficacy is held at a constant rate until it drops to zero after the user-defined duration.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Box_Duration	float	0	100000	100	The box duration of the effect in days. Specify how this effect decays over time using one of the Waning effect classes.	{ "Killing_Config": { "Box_Duration": 40, "Decay_Time_Constant": 0, "Initial_Effect": 0, "class": "WaningEffectBoxExponential" } }
Initial_Effect	float	0	1	-1	Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes.	{ "Usage_Config": { "class": "WaningEffectRandomBox", "Initial_Effect": 1.0, "Expected_Discard_Time": 60 } }

WaningEffectBoxExponential ¶

The initial efficacy is held for a specified duration, then the efficacy decays at an exponential rate.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Box_Duration	float	0	100000	100	The box duration of the effect in days. Specify how this effect decays over time using one of the Waning effect classes.	{ "Waning_Config": { "Box_Duration": 3650, "Initial_Effect": 1, "class": "WaningEffectBox" } }
Decay_Time_Constant	float	0	100000	100	The exponential decay length, in days.	{ "Killing_Config": { "Box_Duration": 40, "Decay_Time_Constant": 0, "Initial_Effect": 0, "class": "WaningEffectBoxExponential" } }

WaningEffectConstant ¶

The efficacy is held at a constant rate.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Initial_Effect	float	0	1	-1	Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes.	{ "Usage_Config": { "class": "WaningEffectRandomBox", "Initial_Effect": 1.0, "Expected_Discard_Time": 60 } }

WaningEffectExponential ¶

The efficacy decays at an exponential rate.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Decay_Time_Constant	float	0	100000	100	The exponential decay length, in days.	{ "Killing_Config": { "Box_Duration": 40, "Decay_Time_Constant": 0, "Initial_Effect": 0, "class": "WaningEffectBoxExponential" } }
Initial_Effect	float	0	1	-1	Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes.	{ "Usage_Config": { "class": "WaningEffectRandomBox", "Initial_Effect": 1.0, "Expected_Discard_Time": 60 } }

WaningEffectMapLinear ¶

The efficacy decays based on the time since the start of the intervention. This change is defined by a map of time to efficacy values in which the time between time/value points is linearly interpolated. When the time since start reaches the end of the times in the map, the last value will be used unless the intervention expires. If the time since start is less than the first value in the map, the efficacy will be zero. This can be used to define the shape of a curve whose magnitude is defined by the Initial_Effect multiplier.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Durability_Map	JSON object	NA	NA	NA	The time, in days, since the intervention was distributed and a multiplier for the Initial_Effect.	{ "class": "WaningEffectMapLinearSeasonal", "Initial_Effect": 1.0, "Durability_Map": { "Times": [0.0, 20.0, 21.0, 30.0, 31.0, 365.0], "Values": [1.0, 1.0, 0.0, 0.0, 1.0, 1.0] } }
Expire_At_Durability_Map_End	boolean	0	1	0	Set to 1 to let the intervention expire when the end of the map is reached.	{ "Changing_Effect": { "class": "WaningEffectConstant", "Initial_Effect": 1.0, "Expire_At_Durability_Map_End": 1 } }
Initial_Effect	float	0	1	-1	Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes.	{ "Usage_Config": { "class": "WaningEffectRandomBox", "Initial_Effect": 1.0, "Expected_Discard_Time": 60 } }
Reference_Timer	integer	0	2.15E+0	0	The timestamp at which linear-map should be anchored.	{ "Changing_Effect": { "Initial_Effect": 1.0, "class": "WaningEffectMapLinear", "Expire_At_Durability_Map_End": 1, "Reference_Timer": 1, "Durability_Map": { "Times": [0, 385, 390, 10000], "Values": [0, 0.0, 0.5, 0.5] } } }
Times	array of floats	0	999999	NA	An array of days.	{ "Changing_Effect": { "Initial_Effect": 1.0, "class": "WaningEffectMapLinear", "Expire_At_Durability_Map_End": 1, "Reference_Timer": 1, "Durability_Map": { "Times": [0, 385, 390, 10000], "Values": [0, 0.0, 0.5, 0.5] } } }
Values	array of floats	0	3.40E+3	NA	An array of values to match the defined Times.	{ "Time_Value_Map": { "Times": [1998, 2000, 2003, 2006, 2009], "Values": [ 0, 0.260000, 0.080000, 0.140000, 0.540000 ] } }

WaningEffectMapLinearAge ¶

Similar to WaningEffectMapLinear, except that the efficacy decays based on the age of the individual who owns the intervention instead of the time since the start of the intervention.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Durability_Map	JSON object	NA	NA	NA	The time, in days, since the intervention was distributed and a multiplier for the Initial_Effect.	{ "class": "WaningEffectMapLinearSeasonal", "Initial_Effect": 1.0, "Durability_Map": { "Times": [0.0, 20.0, 21.0, 30.0, 31.0, 365.0], "Values": [1.0, 1.0, 0.0, 0.0, 1.0, 1.0] } }
Initial_Effect	float	0	1	-1	Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes.	{ "Usage_Config": { "class": "WaningEffectRandomBox", "Initial_Effect": 1.0, "Expected_Discard_Time": 60 } }
Times	array of floats	0	125	NA	An array of years.	{ "class": "WaningEffectMapLinearAge", "Initial_Effect": 1.0, "Durability_Map": { "Times": [0.0, 12.99999, 13.0, 125.0], "Values": [0.0, 0.0, 1.0, 1.0] } }
Values	array of floats	0	3.40E+3	NA	An array of values to match the defined Times.	{ "Time_Value_Map": { "Times": [1998, 2000, 2003, 2006, 2009], "Values": [ 0, 0.260000, 0.080000, 0.140000, 0.540000 ] } }

WaningEffectMapLinearSeasonal ¶

Similar to WaningEffectMapLinear, except that the map will repeat itself every 365 days. That is, the time since start will reset to zero once it reaches 365. This allows you to simulate seasonal effects.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Durability_Map	JSON object	NA	NA	NA	The time, in days, since the intervention was distributed and a multiplier for the Initial_Effect.	{ "class": "WaningEffectMapLinearSeasonal", "Initial_Effect": 1.0, "Durability_Map": { "Times": [0.0, 20.0, 21.0, 30.0, 31.0, 365.0], "Values": [1.0, 1.0, 0.0, 0.0, 1.0, 1.0] } }
Initial_Effect	float	0	1	-1	Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes.	{ "Usage_Config": { "class": "WaningEffectRandomBox", "Initial_Effect": 1.0, "Expected_Discard_Time": 60 } }
Times	array of floats	0	365	NA	An array of days.	{ "class": "WaningEffectMapLinearSeasonal", "Initial_Effect": 1.0, "Durability_Map": { "Times": [0.0, 20.0, 21.0, 30.0, 31.0, 365.0], "Values": [1.0, 1.0, 0.0, 0.0, 1.0, 1.0] } }
Values	array of floats	0	3.40E+3	NA	An array of values to match the defined Times.	{ "Time_Value_Map": { "Times": [1998, 2000, 2003, 2006, 2009], "Values": [ 0, 0.260000, 0.080000, 0.140000, 0.540000 ] } }

WaningEffectMapPiecewise ¶

Similar to WaningEffectMapLinear, except that the data is assumed to be constant between time/value points (no interpolation). If the time since start falls between two points, the efficacy of the earlier time point is used.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Durability_Map	JSON object	NA	NA	NA	The time, in days, since the intervention was distributed and a multiplier for the Initial_Effect.	{ "class": "WaningEffectMapLinearSeasonal", "Initial_Effect": 1.0, "Durability_Map": { "Times": [0.0, 20.0, 21.0, 30.0, 31.0, 365.0], "Values": [1.0, 1.0, 0.0, 0.0, 1.0, 1.0] } }
Expire_At_Durability_Map_End	boolean	0	1	0	Set to 1 to let the intervention expire when the end of the map is reached.	{ "Changing_Effect": { "class": "WaningEffectConstant", "Initial_Effect": 1.0, "Expire_At_Durability_Map_End": 1 } }
Initial_Effect	float	0	1	-1	Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes.	{ "Usage_Config": { "class": "WaningEffectRandomBox", "Initial_Effect": 1.0, "Expected_Discard_Time": 60 } }
Reference_Timer	integer	0	2.15E+0	0	The timestamp at which linear-map should be anchored.	{ "Changing_Effect": { "Initial_Effect": 1.0, "class": "WaningEffectMapLinear", "Expire_At_Durability_Map_End": 1, "Reference_Timer": 1, "Durability_Map": { "Times": [0, 385, 390, 10000], "Values": [0, 0.0, 0.5, 0.5] } } }
Times	array of floats	0	999999	NA	An array of days.	{ "Changing_Effect": { "Initial_Effect": 1.0, "class": "WaningEffectMapLinear", "Expire_At_Durability_Map_End": 1, "Reference_Timer": 1, "Durability_Map": { "Times": [0, 385, 390, 10000], "Values": [0, 0.0, 0.5, 0.5] } } }
Values	array of floats	0	3.40E+3	NA	An array of values to match the defined Times.	{ "Time_Value_Map": { "Times": [1998, 2000, 2003, 2006, 2009], "Values": [ 0, 0.260000, 0.080000, 0.140000, 0.540000 ] } }

WaningEffectRandomBox ¶

The efficacy is held at a constant rate until it drops to zero after a user-defined duration. This duration is randomly selected from an exponential distribution where Expected_Discard_Time is the mean.

Parameter	Data type	Minimum	Maximum	Default	Description	Example
Expected_Discard_Time	float	0	100000	100	The mean time, in days, of an exponential distribution of the duration of the effect of an intervention (such as a vaccine or bed net). Specify how this effect decays over time using one of the Waning effect classes.	{ "Usage_Config": { "class": "WaningEffectRandomBox", "Initial_Effect": 1.0, "Expected_Discard_Time": 60 } }
Initial_Effect	float	0	1	-1	Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes.	{ "Usage_Config": { "class": "WaningEffectRandomBox", "Initial_Effect": 1.0, "Expected_Discard_Time": 60 } }

Event list¶

The following list provides possible values for built-in events. These events can be broadcast to nodes to trigger interventions.

Note

Not all events are appropriate for every simulation type.

Births
DiseaseDeaths
EighteenMonthsOld
Emigrating
EnteredRelationship
EveryTimeStep
EveryUpdate
ExitedRelationship
FirstCoitalAct
FourteenWeeksPregnant
GaveBirth
HappyBirthday
HIVNewlyDiagnosed
HIVNonPreARTToART
HIVPreARTToART
HIVSymptomatic
HIVTestedNegative
HIVTestedPositive
Immigrating
InterventionDisqualified
NewClinicalCase
NewInfectionEvent
NewSevereCase
NodePropertyChange
NonDiseaseDeaths
NoTrigger
Pregnant
PropertyChange
ProviderOrdersTBTest
SixWeeksOld
StartedART
STIDebut
STINewInfection
STIPostImmigrating
STIPreEmigrating
StoppedART
TBActivation
TBActivationExtrapulm
TBActivationPostRelapse
TBActivationPresymptomatic
TBActivationSmearNeg
TBActivationSmearPos
TBFailedDrugRegimen
TBMDRTestDefault
TBMDRTestNegative
TBMDRTestPositive
TBPendingRelapse
TBRelapseAfterDrugRegimen
TBRestartHSB
TBStartDrugRegimen
TBStopDrugRegimen
TBTestDefault
TBTestNegative
TBTestPositive
TestPositiveOnSmear
TwelveWeeksPregnant

Input and output file structure¶

The section describes the file structure used for input data files, each of the built-in output report types, and error logs to help debug issues you may encounter.

Input data file structure¶

The input data files described in this section determine the demographics, migration, climate, and other relatively fixed information about the population within each geographic node. These files are in contrast to the population, geographic, and migration parameters in the configuration file that control simulation-wide qualities, such as enabling air migration across all nodes in the simulation.

Generally, you will download and use input data files without modification. For instructions, see Use input data files. However, demographics files can include many user-defined parameter values and will likely require modification. Only the demographics file is required for a simulation.

Each type of input data generally requires both a metadata file that contains provenance information and a binary file that contains the actual data for each node. Some input files included with EMOD were prepared using CIESIN Gridded Population of the World (GPW) population distribution and a corresponding spatial resolution grid (for example, 2.5 arc minutes) to define the initial population and extent of the nodes for country-wide input files. Therefore, the naming convention for this files usually leads with the geographic location, followed by the spatial resolution, and input file type.

All input files include the parameter IdReference in the metadata, which is used to generate the NodeID associated with each node in a simulation. The values for IdReference and NodeID must be the same across all input data files used in a simulation. See Demographics parameters for more information about NodeID generation.

Climate file structure¶

There are two general types of climate files usable by EMOD, namely, climate files generated through actual data, referred to as “climate by data,” and climate files generated from the Koppen classification system, referred to as “climate by Koppen”. Climate data (both types) is contained in a set of two files, a metadata file with header information (<name>.bin.json) and a binary data file (<name>.bin). Both files are required.

The metadata file is a JSON-formatted file that includes a metadata section and a node offsets section. The Metadata parameter contains a JSON object with parameters, some of which are strictly informational and some of which are used by Eradication.exe. However, the informational ones may still be important to understand the provenance and meaning of the data.

In a second section, the NodeOffsets parameter contains a list of hex-encoded 16-byte values used to find the data for each given node (the NodeID).They are not 16-byte offsets, but instead, two 8-byte hex-encoded character strings. This encoding includes the source NodeID. You can map the binary data to its corresponding source NodeID by using the NodeOffset information.

The binary file contains the climate data in a sequential stream. In other words, it presents all the data for the first node, then all the data for the second node, all the way through to the last node.

To use the climate files, you must set Climate_Model to either “CLIMATE_BY_DATA” or “CLIMATE_BY_KOPPEN”, as appropriate, in the configuration file. There are also additional parameters in the configuration file you can use to scale or otherwise modify the data included in the climate files.

Climate by data¶

At this time, the EMOD executable (Eradication.exe) reads land temperature data, but does not use the data in any calculations. IDM clones the air temperature and uses that as the land temperature in the climate data files. If you are going to be constructing your own climate files, we advise you to do the same.

Metadata file¶

The following parameters in the metadata section are informational:

Parameter	Data type	Description
DateCreated	string	The day the file was created.
Author	string	The author of the file.
OriginalDataYears	string	The years from which the original data was derived.
StartDayOfYear	string	The day of the year representing the first day in the climate file.
DataProvenance	string	The source of the data.

The following parameters in the metadata section are used by Eradication.exe:

Parameter	Data type	Description
IdReference	string	A unique, user-selected string that indicates the method used for generating NodeID values in the input file. For more information, see Input data file structure.
NodeCount	integer	The number of nodes to expect in this file.
DatavalueCount	integer	The number of data values per node. The number must be the same across every node in the binary file.
UpdateResolution	enum	The time resolution of the climate file. Available values are: CLIMATE_UPDATE_YEAR CLIMATE_UPDATE_MONTH CLIMATE_UPDATE_WEEK CLIMATE_UPDATE_DAY CLIMATE_UPDATE_HOUR

An example of climate by data metadata is as follows:

{
    "Metadata": {
        "DateCreated": "Sun Sep 25 19:02:09 2011",
        "Tool": "createclimateheader.py",
        "Author": "authorName",
        "IdReference": "Gridded world grump2.5arcmin",
        "NodeCount": 1,
        "DatavalueCount": 3650,
        "UpdateResolution": "CLIMATE_UPDATE_DAY",
        "OriginalDataYears": "1990-1993",
        "StartDayOfYear": "January 1",
        "DataProvenance": "47 consecutive months of data were used to generate one average year of data that is repeated for 10 years"
    },
    "NodeOffsets": "144B07A400000000"
}

Binary file¶

The binary file is a stream of 4-byte floating point values that contain the data value at the data count position for a given node, running from 1 to the maximum data count value.

The binary format is as follows:

Climate by Koppen¶

The Koppen classification system is one of the most widely used climate classification systems. The Koppen classification system makes the assumption that native vegetation is the best expression of climate.

Metadata file¶

The following parameters in the metadata section are informational:

Parameter	Data type	Description
DateCreated	string	The day the file was created.
Author	string	The author of the file.
DataProvenance	string	The source of the data.
Tool	string	The script used to create the file.

The following parameters in the metadata section are used by Eradication.exe:

Parameter	Data type	Description
IdReference	string	A unique, user-selected string that indicates the method used for generating NodeID values in the input file. For more information, see Input data file structure.
NodeCount	integer	The number of nodes to expect in this file.

An example of climate by Koppen metadata is as follows:

{
    "Metadata": {
        "DateCreated": "Sun Sep 25 19:08:52 2011",
        "Tool": "createclimateheader.py",
        "Author": "authorName",
        "IdReference": "Gridded world grump2.5arcmin",
        "NodeCount": 2,
        "DataProvenance": "Köppen-Geiger Classification System from http://koeppen-geiger.vu-wien.ac.at/"
    },
    "NodeOffsets": "157D075200000000157E07520000000"
}

Binary file¶

The binary file parameters use the naming convention below to store the data.

Parameter	Data type	Description
KoppenIndexX	integer, 4 bytes	The Koppen Index value, with X running from 1 to the maximum number of nodes.

The binary format is as follows:

Migration file structure¶

There are four types of migration files that can be used by EMOD, namely, local migration, regional migration, air migration and sea migration. For all types, migration data is contained in a set of two files, a metadata file with header information and a binary data file. Both files are required. The structure of each of these files is nearly the same for all migration types, with the only exception being the number of columns in the binary file.

The metadata file is a JSON-formatted file that includes a metadata section and a node offsets section. The Metadata parameter contains a JSON object with parameters, some of which are strictly informational and some of which are used by Eradication.exe. However, the informational ones may still be important to understand the provenance and meaning of the data.

The following parameters in the metadata section are informational:

Parameter	Data type	Description
DateCreated	string	The day the file was created.
Author	string	The author of the file.
Tool	string	The script used to create the file.

The following parameters in the metadata section are used by Eradication.exe:

Parameter	Data type	Description
IdReference	string	A unique, user-selected string that indicates the method used for generating NodeID values in the input file. For more information, see Input data file structure.
NodeCount	integer	The number of nodes to expect in this file.
DatavalueCount	integer	The number of data values per node. The number must be the same across every node in the binary file.

In a second section, the NodeOffsets parameter contains a list of hex-encoded 16-byte values used to find the data for each given node (the NodeID).They are not 16-byte offsets, but instead, two 8-byte hex-encoded character strings. This encoding includes the source NodeID. You can map the binary data to its corresponding source NodeID by using the NodeOffset information.

The binary file contains the migration rate data in a sequential stream. In other words, it presents all the data for the first node, then all the data for the second node, all the way through to the last node. The data is laid out in rows and columns, with each row corresponding to a node and the number of columns varying based on the migration type.

The following diagram shows how migration data is laid out in rows, with each row corresponding to a source node and each column corresponding to the destination node.

Migration rate is that fraction of the node’s population that is migrating out of the node per day. Units are per person per day, meaning the number of people migrating per day divided by the total population of the node. For example, if a node had 1,000 people and a migration rate of .01, there would be .01 * 1,000 = 10 people migrating out per day, but if the node had 10,000 people in the node and a migration rate of .01, it would .01 * 10,000 = 100 people migrating out per day. You can adjust this rate using configuration parameters for scaling (these begin with x).

The binary file contains a stream of 4-byte unsigned integers that identify the destination node, followed by a stream of 8-byte double floating point values that contain the rate associated with the destination node (per person per day), running from 1 to the total number of nodes.

To use the migration files, you must set Migration_Model in the configuration file to a valid migration type except “NO_MIGRATION”. Each migration types also requires you to set another parameter to enable the particular type of migration selected. There are also additional parameters in the configuration file you can use to scale or otherwise modify the data included in the climate files.

Local¶

Local migration describes the foot travel movement of people into adjacent nodes. A local migration file is required for simulations that support more than one node. You must also set the Enable_Local_Migration parameter in the configuration file to 1. For each location, the local migration file represents up to eight adjacent destination nodes, with 0 used as the value for unused nodes.

The following diagram shows the format for the local migration binary file data:

Regional¶

Regional migration describes migration that occurs on a road or rail network for a simulation. If a node is not part of the network, the regional migration of individuals to and from that node considers the closest road hub city. A Voronoi tiling based on road hubs is constructed of the region, with each non-hub connected to the hub of its tile. These connections are created when the migration file is constructed. They are not performed at runtime.

A regional migration file is required for simulations that cover a geography that is large enough that road/rail migration is relevant. You must also set the Enable_Regional_Migration parameter in the configuration file to 1. For each location, the regional migration file represents up to 30 adjacent destination nodes, with 0 used as the value for unused nodes.

The following diagram shows the format for the regional migration binary file data:

Air¶

Air migration describes migration that occurs by airplane travel. An air migration file is usually required for simulations of an entire country or larger geographies. You must also set the Enable_Air_Migration parameter in the configuration file to 1. For each location, the air migration file represents up to 60 adjacent destination nodes, with 0 used as the value for unused nodes.

The following diagram shows the format for the air migration binary file data:

Sea¶

Sea migration describes migration that occurs by ship travel. Unlike the other migration files, the sea migration file only contains information for the nodes that are seaports. A sea migration file does not contain every node like local, regional or air migration files do. You must also set the Enable_Sea_Migration parameter in the configuration file to 1. For each location, the sea migration file represents up to five adjacent destination nodes, with 0 used as the value for unused nodes.

The following diagram shows the format for the sea migration binary file data:

Load-balancing file structure¶

If you are running a large simulation with multiple nodes, you may want to use a load balancing file to distribute the computing load among multiple cores. This can be especially helpful if the nodes vary considerably in size and, therefore, processing time. If no load balancing file is submitted, the Eradication.exe allocates simulation nodes to cores according to a checkerboard pattern.

For each simulation, the load balancing file contains information about the relative level of processing time required for each geographical node. The Eradication.exe can then allocate nodes to processors in such a way that the total processing required is evenly distributed across all processors.

To use a load balancing file, you must set Load_Balance_Filename to the path to the load balancing file, relative to the input file directory. Load-balancing files can be JavaScript Object Notation (JSON) files, which are preferred, or binary files.

JSON file¶

A JSON load-balancing file contains the Load_Balance_Scheme_Nodes_On_Core_Matrix parameter, assigned an array of arrays, each of which represents a core and lists the NodeID of each node to be processed on that core. If any node contained in the demographics file is not listed in the load-balancing file, it will be processed on the first core.

For example, the load-balancing file shown below distributes the processing for 100 nodes across 4 cores, assigning more nodes to particular cores when those nodes require less processing time.

{
    "Load_Balance_Scheme_Nodes_On_Core_Matrix": [
        [1, 2, 3, 4, 5, 11, 12, 13, 14, 15, 21, 22, 23, 24, 25, 31, 32, 33, 34, 35, 45],
        [6, 7, 8, 9, 10, 16, 17, 18, 19, 20, 26, 27, 28, 29, 30, 36, 37, 38, 39, 40, 46, 47, 48, 49, 50, 56, 57, 58, 59, 60, 67, 68, 69, 70],
        [61, 62, 63, 64, 65, 66, 71, 72, 73, 74, 75, 76, 77, 81, 82, 83, 84, 85, 86, 87, 91, 92, 93, 94, 95, 96, 97],
        [41, 42, 43, 44, 51, 52, 53, 54, 55, 78, 79, 80, 88, 89, 90, 98, 99, 100]
    ]
}

Binary file¶

A binary load-balancing file starts with an initial unsigned 4-byte integer that indicates the number of nodes. Following that value is a series of 4-byte unsigned integers representing the NodeID values. The number of values will be equal to the previously read number of nodes. Following that, another series of 4-byte floating point values, with each value representing the relative processing time required for each geographic node. Again, the number of values will be equal to the previously read number of nodes. The series of values are set up such that the $i^{th}$ entry in the series is equal to the cumulative proportion of the processing load for all the previous nodes 0 through $i$.

The cumulative nature of each node’s value does not mean each node is assigned that amount for its processing load. Rather, it is a way to make the internal calculations more efficient. For example, if you had ten nodes and each node was assigned 10% of the load, the values assigned from node0 to node9 would be the following: 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1.0.

The following diagram shows the format for the binary load-balancing file data:

Output report structure¶

This topic defines various data output reports that the EMOD executable (Eradication.exe) can produce. These reports are built in to the Eradication.exe and are either always output with each simulation, such as inset chart, or reports that you can configure to be included with the output. All output reports can be found in the output directory of the working directory, for example, 1_Generic_Seattle_MultiNodeoutput.

If the available output reports do not meet your needs, you can use custom reporters that are EMODules separate from the Eradication.exe code. There are several provided in the EMOD GitHub repository or you can even write your own custom reporter to post- process and format data as you desire into a new output report. For more information, see Custom reporters.

Inset chart output report¶

The inset chart output report is output with every simulation. It is a JSON-formatted file with the channel output results of the simulation, consisting of simulation-wide averages by time step. The channels in an inset chart are fully specified by the simulation type and cannot be altered without making changes to the EMOD source code. The file name is InsetChart.json. You can use the information contained in the file to create a chart, but EMOD does not automatically output a chart.

The file contains a header and a channels section.

Header¶

The header section contains the following parameters.

Parameter	Data type	Description
DateTime	string	The time stamp indicating when the report was generated.
DTK_Version	string	The version of EMOD used.
Report_Type	string	The type of output report.
Report_Version	string	The format version of the report.
Start_Time	integer	The time noted in days when the simulation begins.
Simulation_Timestep	integer	The number of days in each time step.
Timesteps	integer	The number of time steps in this simulation.
Channels	integer	The number of channels in the simulation.

Channels¶

The channels section contains the following parameters.

Parameter	Data type	Description
<Channel_Title>	string	The title of the particular channel.
Units	string	The units used for this channel.
Data	array	A list of the channel data at each time step.

Example¶

The following is a sample of an InsetChart.json file.

{
    "Header": {
        "DateTime": "Thu Dec 03 11:28:34 2015",
        "DTK_Version": "5738 HIV-Ongoing 2015/11/18 13:00:39",
        "Report_Type": "InsetChart",
        "Report_Version": "3.2",
        "Start_Time": 0,
        "Simulation_Timestep": 1,
        "Timesteps": 5,
        "Channels": 17
    },
    "Channels": {
        "Births": {
            "Units": "Births",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Campaign Cost": {
            "Units": "USD",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Cumulative Infections": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Cumulative Reported Infections": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Disease Deaths": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Exposed Population": {
            "Units": "Exposed Fraction",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Human Infectious Reservoir": {
            "Units": "Total Infectivity",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Infected": {
            "Units": "Infected Fraction",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Infectious Population": {
            "Units": "Infectious Fraction",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Log Prevalence": {
            "Units": "Log Prevalence",
            "Data": [-10, -10, -10, -10, -10]
        },
        "New Infections": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "New Reported Infections": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Daily (Human) Infection Rate": {
            "Units": "Infection Rate",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Recovered Population": {
            "Units": "Recovered (Immune) Fraction",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Statistical Population": {
            "Units": "Population",
            "Data": [
                900,
                900,
                900,
                900,
                900
            ]
        },
        "Susceptible Population": {
            "Units": "Susceptible Fraction",
            "Data": [
                1,
                1,
                1,
                1,
                1
            ]
        },
        "Waning Population": {
            "Units": "Waning Immunity Fraction",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        }
    }
}

Binned output report¶

The binned output report is a JSON-formatted file where the channel data has been sorted into bins. It is very similar to an inset chart. However, with the binned report, channels are broken down into sub-channels (bins) based on various criteria. For example, instead of having a single prevalence channel, you might have prevalence in the “0-3 years old bin” and the “4-6 years old bin, and so forth. The file name is BinnedReport.json.

To generate the binned report, set the Enable_Demographics_Reporting configuration parameter to 1. The demographics summary output report will also be generated.

The file contains a header and a channels section.

Header¶

The header section contains the following parameters.

Parameter

Data type

Description

DateTime

string

The time stamp indicating when the report was generated.

DTK_Version

string

The version of EMOD used.

Report_Type

string

The type of output report.

Report_Version

string

The format version of the report.

Timesteps

integer

The number of time steps in this simulation.

Channels

integer

The number of channels in the simulation.

Subchannel_Metadata

nested JSON object

Metadata that describes the bins and axis information. The metadata includes the following parameters:

Parameter	Data type	Description
AxisLabels	array of strings	The name of each axis.
NumBinsPerAxis	array of integers	The number of bins per axis.
ValuesPerAxis	array of integers	The maximum value for each bin in the axis. For example, if the axis is “Age”, the values are age maximums in days.
MeaningPerAxis	array of strings	The “meaning” of each element in ValuesPerAxis. For example, if the axis is “Age”, this shows those values binned by age range, such as younger than 5 years, 5 to 9, and so on.

Channels¶

The channels section contains the following parameters.

Parameter	Data type	Description
<Channel_Title>	string	The title of the particular channel.
Units	string	The units used for this channel.
Data	array	A list of the channel data at each time step.

Example¶

The following is a sample of an BinnedReport.json file.

{
    "Header": {
        "DateTime": "Wed Oct 01 14:49:22 2014",
        "DTK_Version": "4284 trunk 2014/09/23 11:50:52",
        "Report_Version": "2.1",
        "Timesteps": 150,
        "Subchannel_Metadata": {
            "AxisLabels": [
                ["Age"]
            ],
            "NumBinsPerAxis": [
                [21]
            ],
            "ValuesPerAxis": [
                [
                    [1825, 3650, 5475, 7300, 9125, 10950, 12775, 14600, 16425, 18250, 20075, 21900, 23725, 25550, 27375, 29200, 31025, 32850, 34675, 36500, 999999]
                ]
            ],
            "MeaningPerAxis": [
                [
                    ["<5", "5-9", "10-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95-99", ">100"]
                ]
            ]
        },
        "Channels": 4
    },
    "Channels": {
        "Disease Deaths": {
            "Units": "",
            "Data": [
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            ]
        },
        "New Infections": {
            "Units": "",
            "Data": [
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                [281, 281, 281, 281, 281, 281, 280, 280, 281, 281, 281, 281, 281, 282, 282, 282, 282, 282, 282, 282, 283, 283, 282, 282, 282, 283, 283, 283, 283, 283, 283, 283, 283, 283, 283, 282, 281, 281, 282, 282, 281, 281, 282, 282, 282, 283, 283, 283, 283, 283, 283, 284, 284, 284, 284, 285, 285, 285, 286, 286, 286, 286, 286, 286, 286, 286, 286, 286, 286, 286, 286, 286, 287, 287, 287, 287, 286, 286, 286, 286, 285, 285, 285, 285, 285, 285, 286, 286, 286, 286, 286, 287, 287, 287, 287, 287, 287, 287, 287, 287, 286, 286, 286, 285, 285, 285, 285, 285, 286, 284, 284, 284, 284, 283, 282, 283, 283, 283, 283, 283, 283, 282, 282, 282, 282, 282, 282, 282, 281, 282, 282, 282, 282, 282, 282, 281, 281, 281, 281, 281, 281, 283, 283, 283, 284, 283, 283, 282, 282, 282],
                [218, 218, 218, 218, 218, 218, 219, 219, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 219, 219, 219, 219, 219, 219, 219, 219, 218, 216, 216, 215, 215, 215, 215, 214, 214, 214, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 214, 214, 214, 214, 214, 214, 214, 214, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 214, 214, 214, 214, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 216, 216, 216, 217, 217, 216, 218, 218, 218, 218, 219, 220, 220, 220, 220, 220, 220, 220, 221, 221, 221, 221, 221, 220, 220, 221, 221, 220, 220, 220, 220, 220, 221, 221, 221, 221, 221, 221, 221, 221, 222, 222, 223, 223, 224, 224, 224],
                [188, 188, 188, 188, 188, 188, 188, 188, 189, 189, 188, 188, 188, 188, 188, 187, 187, 187, 187, 186, 186, 184, 182, 182, 182, 182, 182, 182, 181, 181, 182, 184, 184, 185, 185, 186, 187, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 189, 189, 189, 188, 188, 188, 188, 187, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 186, 186, 186, 187, 186, 186, 186, 186, 186, 185, 184, 184, 184, 184, 184, 184, 184, 183, 183, 184, 184, 184, 184, 184, 184, 184, 184, 185, 185, 185, 185, 185, 185, 185, 185, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 185, 185, 185, 185, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186],
                [158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 159, 159, 159, 159, 159, 160, 160, 160, 160, 161, 161, 163, 165, 165, 165, 165, 165, 165, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 164, 164, 165, 165, 165, 165, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 168, 168, 168, 168, 169, 169, 169, 169, 169, 170, 171, 171, 171, 171, 171, 171, 171, 170, 170, 170, 170, 168, 168, 168, 168, 168, 168, 168, 167, 166, 166, 165, 165, 165, 165, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 165, 165, 165, 165, 165, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 163, 163, 163, 163, 163, 163, 163],
                [115, 115, 115, 114, 114, 114, 114, 114, 114, 114, 114, 114, 113, 113, 113, 113, 112, 112, 112, 111, 111, 111, 111, 111, 111, 111, 110, 110, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 110, 110, 110, 110, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 109, 109, 109, 109, 109, 109, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 106, 106, 106, 106, 108, 108, 108, 108, 110, 109, 109, 109, 109, 109, 109, 110, 111, 111, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 113, 113, 113, 113, 113, 114, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 114, 114, 114, 114, 114, 114, 114],
                [95, 95, 95, 96, 96, 96, 96, 96, 96, 96, 96, 96, 97, 97, 97, 97, 98, 98, 98, 99, 99, 99, 99, 99, 99, 99, 100, 100, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 103, 103, 103, 103, 103, 103, 104, 104, 104, 104, 104, 104, 103, 103, 103, 103, 103, 103, 103, 104, 104, 104, 103, 103, 103, 103, 103, 103, 103, 104, 104, 104, 104, 104, 104, 104, 104, 104, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105],
                [78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 77, 77, 77, 77, 77, 77, 77, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 75, 75, 75, 75, 75, 75, 75, 75, 75, 76, 76, 75, 75, 75, 75, 75, 75, 75, 75, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 74, 75, 75, 75, 75, 75, 75, 75, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74],
                [72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 71, 71, 71, 71, 71, 71, 71, 72, 72, 72, 72, 72, 72, 72, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 72, 72, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 74, 74, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74],
                [49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 50, 50, 51, 51, 51, 52, 52, 52, 52, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50],
                [39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 43, 43, 43, 43, 43, 43, 43, 43, 43, 42, 42, 42, 42, 42],
                [30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29],
                [119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121]
            ]
        }
    }
}

Demographic summary output report¶

The demographic summary output report is a JSON-formatted file with the demographic channel output results of the simulation, consisting of simulation-wide averages by time step. The format is identical to the inset chart output report, except the channels reflect demographic categories, such as gender ratio. The file name is DemographicsSummary.json.

To generate the demographics summary report, set the Enable_Demographics_Reporting configuration parameter to 1. The binned output report will also be generated.

The file contains a header and a channels section.

Header¶

The header section contains the following parameters.

Parameter	Data type	Description
DateTime	string	The time stamp indicating when the report was generated.
DTK_Version	string	The version of EMOD used.
Report_Type	string	The type of output report.
Report_Version	string	The format version of the report.
Start_Time	integer	The time noted in days when the simulation begins.
Simulation_Timestep	integer	The number of days in each time step.
Timesteps	integer	The number of time steps in this simulation.
Channels	integer	The number of channels in the simulation.

Channels¶

The channels section contains the following parameters.

Parameter	Data type	Description
<Channel_Title>	string	The title of the particular demographic channel.
Units	string	The units used for this channel.
Data	array	A list of the channel data at each time step.

Example¶

The following is a sample of a DemographicsSummary.json file.

{
    "Header": {
        "DateTime": "Mon Mar 16 07:45:10 2015",
        "DTK_Version": "4777 v2.0-HIV 2015/02/26 10:51:25",
        "Report_Type": "InsetChart",
        "Report_Version": "3.2",
        "Start_Time": 0,
        "Simulation_Timestep": 1,
        "Timesteps": 5,
        "Channels": 28
    },
    "Channels": {
        "Average Age": {
            "Units": "",
            "Data": [
                8592.415039063,
                8593.427734375,
                8594.439453125,
                8595.41796875,
                8596.412109375
            ]
        },
        "Gender Ratio (fraction male)": {
            "Units": "",
            "Data": [
                0.5350999832153,
                0.5350999832153,
                0.5350999832153,
                0.5350999832153,
                0.5350999832153
            ]
        },
        "New Births": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "New Natural Deaths": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Population Age 10-14": {
            "Units": "",
            "Data": [
                1203,
                1204,
                1202,
                1203,
                1203
            ]
        },
        "Population Age 15-19": {
            "Units": "",
            "Data": [
                1056,
                1057,
                1059,
                1059,
                1059
            ]
        },
        "Population Age 20-24": {
            "Units": "",
            "Data": [
                810,
                810,
                810,
                809,
                809
            ]
        },
        "Population Age 25-29": {
            "Units": "",
            "Data": [
                732,
                732,
                732,
                732,
                732
            ]
        },
        "Population Age 30-34": {
            "Units": "",
            "Data": [
                540,
                539,
                539,
                539,
                539
            ]
        },
        "Population Age 35-39": {
            "Units": "",
            "Data": [
                410,
                411,
                411,
                411,
                411
            ]
        },
        "Population Age 40-44": {
            "Units": "",
            "Data": [
                351,
                351,
                351,
                352,
                352
            ]
        },
        "Population Age 45-49": {
            "Units": "",
            "Data": [
                294,
                294,
                294,
                294,
                294
            ]
        },
        "Population Age 5-9": {
            "Units": "",
            "Data": [
                1599,
                1597,
                1598,
                1597,
                1599
            ]
        },
        "Population Age 50-54": {
            "Units": "",
            "Data": [
                201,
                201,
                201,
                201,
                201
            ]
        },
        "Population Age 55-59": {
            "Units": "",
            "Data": [
                194,
                194,
                194,
                193,
                193
            ]
        },
        "Population Age 60-64": {
            "Units": "",
            "Data": [
                163,
                163,
                163,
                164,
                164
            ]
        },
        "Population Age 65-69": {
            "Units": "",
            "Data": [
                111,
                111,
                111,
                111,
                111
            ]
        },
        "Population Age 70-74": {
            "Units": "",
            "Data": [
                104,
                104,
                104,
                103,
                103
            ]
        },
        "Population Age 75-79": {
            "Units": "",
            "Data": [
                86,
                86,
                86,
                87,
                87
            ]
        },
        "Population Age 80-84": {
            "Units": "",
            "Data": [
                55,
                55,
                55,
                55,
                55
            ]
        },
        "Population Age 85-89": {
            "Units": "",
            "Data": [
                61,
                61,
                61,
                61,
                61
            ]
        },
        "Population Age 90-94": {
            "Units": "",
            "Data": [
                49,
                49,
                49,
                49,
                49
            ]
        },
        "Population Age 95-99": {
            "Units": "",
            "Data": [
                30,
                30,
                30,
                30,
                30
            ]
        },
        "Population Age <5": {
            "Units": "",
            "Data": [
                1829,
                1829,
                1828,
                1828,
                1826
            ]
        },
        "Population Age >100": {
            "Units": "",
            "Data": [
                122,
                122,
                122,
                122,
                122
            ]
        },
        "Possible Mothers": {
            "Units": "",
            "Data": [
                1912,
                1912,
                1912,
                1913,
                1913
            ]
        },
        "Pseudo-Population": {
            "Units": "",
            "Data": [
                10000,
                10000,
                10000,
                10000,
                10000
            ]
        },
        "Statistical Population": {
            "Units": "",
            "Data": [
                10000,
                10000,
                10000,
                10000,
                10000
            ]
        }
    }
}

Property output report¶

The property output report is a JSON-formatted file with the channel output results of the simulation, defined by the groups set up using IndividualProperties in the demographics file. See Configure heterogeneity using individual and node properties. For example, it allows you to compare disease deaths for people in the high risk group versus the low risk group. If you want to aggregate the data, you must create a script for aggregating information. The file name is PropertyReport.json.

To generate the property report, set the Enable_Property_Output configuration parameter to 1.

The file contains a header and a channels section.

Header¶

The header section contains the following parameters.

Parameter	Data type	Description
DateTime	string	The time stamp indicating when the report was generated.
DTK_Version	string	The version of EMOD used.
Report_Type	string	The type of output report.
Report_Version	string	The format version of the report.
Start_Time	integer	The time noted in days when the simulation begins.
Simulation_Timestep	integer	The number of days in each time step.
Timesteps	integer	The number of time steps in this simulation.
Channels	integer	The number of channels in the simulation.

Channels¶

The channels section contains the following parameters.

Parameter	Data type	Description
<Channel_Title>	string	The title of the particular property channel. The channel titles use the following conventions: for a single property, <channel type>:<property>:<value>, and for multiple properties, <channel type>:<property 1>:<value>,<property 2>:<value>. For example, Infected:Accessibility:Easy or New Infections:Accessibility:Difficult,Risk:High.
Units	string	The units used for this channel.
Data	array	A list of the channel data at each time step.

Example¶

The following is a sample of a PropertyReport.json file.

{
    "Header": {
        "DateTime": "Mon Feb 15 21:49:24 2016",
        "DTK_Version": "5538 trunk 2015/08/07 14:40:43",
        "Report_Type": "InsetChart",
        "Report_Version": "3.2",
        "Start_Time": 0,
        "Simulation_Timestep": 1,
        "Timesteps": 10,
        "Channels": 8
    },
    "Channels": {
        "Disease Deaths:Accessibility:Easy": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Disease Deaths:Accessibility:Hard": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Infected:Accessibility:Easy": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Infected:Accessibility:Hard": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            ]
        },
        "New Infections:Accessibility:Easy": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            ]
        },
        "New Infections:Accessibility:Hard": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Statistical Population:Accessibility:Easy": {
            "Units": "",
            "Data": [
                6946,
                6946,
                6946,
                6946,
                6946,
                6946,
                6946,
                6946,
                6946,
                6946
            ]
        },
        "Statistical Population:Accessibility:Hard": {
            "Units": "",
            "Data": [
                3054,
                3054,
                3054,
                3054,
                3054,
                3054,
                3054,
                3054,
                3054,
                3054
            ]
        }
    }
}

Spatial output report¶

The spatial output report breaks the channel data down per node, rather than across the entire simulation. It is a set of binary files, consisting of one file per channel. For each value set in the Spatial_Output_Channels configuration parameter array, a binary file with the name convention SpatialReport_<channel>.bin is generated. In addition, Enable_Spatial_Output must be set to 1.

The binary format of the file consists of a stream of 4-byte integers followed by a stream of 4-byte floating point values. The first value is a 4-byte integer representing the number of nodes in the file and the second is a 4-byte integer that contains the number of time steps in the file. Following these two values is a stream of 4-byte integers that contain the node ID values in the order they will appear in the rest of the file. Following the node IDs is an array of 4-byte floating point values that represent the output values at the first time step for each node. The next array contains the values at the second time step, and so on.

The following diagram shows the format for data in the spatial output report file:

Error and logging files¶

When you run a simulation, EMOD will output basic error and logging information to help track the progress and help debug any issues that may occur. If you run the simulation on an HPC cluster, the cluster will generate additional logging and error files. See Troubleshooting EMOD simulations if you need help resolving an error.

Status¶

A status.txt file will be saved to the working directory that provides one output line per time step and includes the total run time of the simulation. A simulation with 50 time steps will look something like this:

Beginning Simulation...
of 50 steps complete.
of 50 steps complete.
of 50 steps complete.
of 50 steps complete.
of 50 steps complete.
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Standard output¶

When you run a simulation on an HPC cluster, it will generate a standard output logging file (StdOut.txt) in the working directory that captures all standard output messages. When you run a simulation locally, the Command Prompt window will display the same information. If you want to save this information to a text file instead, you can append > stdout.txt to the command used to run the local EMOD simulation to redirect the console output to the file specified.

The file contains information about a particular simulation, such as the EMOD version used and the files in use, as well as other initialization information, including the default logging level and the logging levels set for particular modules. The file follows that information with log output using the following format: <timestep><HPC rank><log level><module><message>.

By default, the logging level is set to “INFO”. If you want to change the logging level, see Set log levels.

For example:

C:\EMOD\CampaignTesting\00_DefaultDemographics>..\Eradication.exe -C config.json -I ../input
Intellectual Ventures(R)/EMOD Disease Transmission Kernel 2.8.1331.0
Built on Sep 30 2016 08:39:43 by cwiswell from master (482fdae) checked in on 2016-09-28 14:47:20 -0700
Supports sim_types: GENERIC, VECTOR, MALARIA, AIRBORNE, POLIO, TB, TBHIV, STI, HIV, PY.
Using config file: config.json
Using input path: ../input
Using output path: output
using dll path:
--python-script-path (-P) not on command line - not using embedded python
Initializing environment...
Log-levels:
        Default -> INFO
        Eradication -> INFO
00:00 [0] [I] [Eradication] Loaded Configuration...
00:00 [0] [I] [Eradication] 99 parameters found.
00:00 [0] [I] [Eradication] Initializing Controller...
00:00 [0] [I] [Controller] DefaultController::execute_internal()...
00:00 [0] [I] [Simulation] Using PSEUDO_DES random number generator.
00:00 [0] [I] [Controller] DefaultController::execute_internal() populate simulation...
00:00 [0] [I] [Simulation] Campaign file name identified as: campaign.json
00:00 [0] [I] [NodeDemographics] Using default 3x3 torus geography
00:00 [0] [I] [Climate] Initialize
00:00 [0] [I] [LoadBalanceScheme] Using Checkerboard Load Balance Scheme.
00:01 [0] [I] [Simulation] Looking for campaign file campaign.json
00:01 [0] [I] [Simulation] Found campaign file successfully.
00:01 [0] [I] [DllLoader] ReadEmodulesJson: no file, returning.
00:01 [0] [I] [DllLoader] dllPath not passed in, getting from EnvPtr
00:01 [0] [I] [DllLoader] Trying to copy from string to wstring.
00:01 [0] [I] [DllLoader] DLL ws root path:
00:01 [0] [W] [Simulation] Failed to load intervention emodules for SimType: GENERIC_SIM from path: interventions
00:01 [0] [I] [JsonConfigurable] Using the default value ( "Number_Repetitions" : -1 ) for unspecified parameter.
00:01 [0] [I] [JsonConfigurable] Using the default value ( "Timesteps_Between_Repetitions" : -1 ) for unspecified parameter.
00:01 [0] [I] [JsonConfigurable] Using the default value ( "Intervention_Name" : "struct Kernel::BaseIntervention" ) for unspecified parameter.
00:01 [0] [I] [JsonConfigurable] Using the default value ( "Intervention_Name" : "struct Kernel::BaseIntervention" ) for unspecified parameter.
00:01 [0] [I] [JsonConfigurable] Using the default value ( "Intervention_Name" : "struct Kernel::BaseIntervention" ) for unspecified parameter.
00:01 [0] [I] [Simulation] populateFromDemographics() created 9 nodes
00:01 [0] [I] [Simulation] populateFromDemographics() generated 9 nodes.
00:01 [0] [I] [Simulation] Rank 0 contributes 9 nodes...
00:01 [0] [I] [Simulation] Merging node rank maps...
00:01 [0] [I] [Simulation] Merged rank 0 map now has 9 nodes.
00:01 [0] [I] [Simulation] Rank 0 map contents:
{ NodeRankMap:
[1,000000000C10A960]
[2,000000000BF4EAF0]
[3,000000000BFA2250]
[4,000000000BFF1C10]
[5,000000000BE37B60]
[6,000000000BE87380]
[7,000000000BEE74F0]
[8,000000000B721980]
[9,000000000B7AE280]
}

00:01 [0] [I] [Simulation] Initialized 'InsetChart.json' reporter
00:01 [0] [I] [Simulation] Initialized 'BinnedReport.json' reporter
00:01 [0] [I] [Simulation] Initialized 'DemographicsSummary.json' reporter
00:01 [0] [I] [Simulation] Update(): Time: 1.0 Rank: 0 StatPop: 900 Infected: 0
00:01 [0] [I] [SimulationEventContext] Time for campaign event. Calling Dispatch...
00:01 [0] [I] [SimulationEventContext] 9 node(s) visited.
00:01 [0] [I] [JsonConfigurable] Using the default value ( "Intervention_Name" : "struct Kernel::BaseIntervention" ) for unspecified parameter.
00:01 [0] [I] [JsonConfigurable] Using the default value ( "Intervention_Name" : "struct Kernel::BaseIntervention" ) for unspecified parameter.
00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 0 'OutbreakIndividual' interventions at node 1
00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 2 'OutbreakIndividual' interventions at node 2
00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 0 'OutbreakIndividual' interventions at node 3
00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 0 'OutbreakIndividual' interventions at node 4
00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 0 'OutbreakIndividual' interventions at node 5
00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 0 'OutbreakIndividual' interventions at node 6
00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 0 'OutbreakIndividual' interventions at node 7
00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 1 'OutbreakIndividual' interventions at node 8
00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 1 'OutbreakIndividual' interventions at node 9
00:01 [0] [I] [Simulation] Update(): Time: 2.0 Rank: 0 StatPop: 900 Infected: 4
00:01 [0] [I] [Simulation] Update(): Time: 3.0 Rank: 0 StatPop: 900 Infected: 18
00:01 [0] [I] [Simulation] Update(): Time: 4.0 Rank: 0 StatPop: 900 Infected: 63
00:01 [0] [I] [Simulation] Update(): Time: 5.0 Rank: 0 StatPop: 900 Infected: 161
00:01 [0] [I] [Simulation] Update(): Time: 6.0 Rank: 0 StatPop: 900 Infected: 232
00:01 [0] [I] [Simulation] Update(): Time: 7.0 Rank: 0 StatPop: 900 Infected: 203
00:01 [0] [I] [Simulation] Update(): Time: 8.0 Rank: 0 StatPop: 900 Infected: 165
00:01 [0] [I] [Simulation] Update(): Time: 9.0 Rank: 0 StatPop: 900 Infected: 132
00:01 [0] [I] [Simulation] Update(): Time: 10.0 Rank: 0 StatPop: 900 Infected: 110
00:01 [0] [I] [Simulation] Update(): Time: 11.0 Rank: 0 StatPop: 900 Infected: 83
00:01 [0] [I] [Simulation] Update(): Time: 12.0 Rank: 0 StatPop: 900 Infected: 69
00:01 [0] [I] [Simulation] Update(): Time: 13.0 Rank: 0 StatPop: 900 Infected: 54
00:01 [0] [I] [Simulation] Update(): Time: 14.0 Rank: 0 StatPop: 900 Infected: 40
00:01 [0] [I] [Simulation] Update(): Time: 15.0 Rank: 0 StatPop: 900 Infected: 30
00:01 [0] [I] [Simulation] Update(): Time: 16.0 Rank: 0 StatPop: 900 Infected: 22
00:01 [0] [I] [Simulation] Update(): Time: 17.0 Rank: 0 StatPop: 900 Infected: 17
00:01 [0] [I] [Simulation] Update(): Time: 18.0 Rank: 0 StatPop: 900 Infected: 14
00:01 [0] [I] [Simulation] Update(): Time: 19.0 Rank: 0 StatPop: 900 Infected: 12
00:01 [0] [I] [Simulation] Update(): Time: 20.0 Rank: 0 StatPop: 900 Infected: 9
00:01 [0] [I] [Simulation] Update(): Time: 21.0 Rank: 0 StatPop: 900 Infected: 7
00:01 [0] [I] [Simulation] Update(): Time: 22.0 Rank: 0 StatPop: 900 Infected: 7
00:01 [0] [I] [Simulation] Update(): Time: 23.0 Rank: 0 StatPop: 900 Infected: 7
00:01 [0] [I] [Simulation] Update(): Time: 24.0 Rank: 0 StatPop: 900 Infected: 5
00:01 [0] [I] [Simulation] Update(): Time: 25.0 Rank: 0 StatPop: 900 Infected: 3
00:01 [0] [I] [Simulation] Update(): Time: 26.0 Rank: 0 StatPop: 900 Infected: 3
00:01 [0] [I] [Simulation] Update(): Time: 27.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 28.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 29.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 30.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 31.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 32.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 33.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 34.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 35.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 36.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 37.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 38.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 39.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 40.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 41.0 Rank: 0 StatPop: 900 Infected: 1
00:02 [0] [I] [Simulation] Update(): Time: 42.0 Rank: 0 StatPop: 900 Infected: 0
00:02 [0] [I] [Simulation] Update(): Time: 43.0 Rank: 0 StatPop: 900 Infected: 0
00:02 [0] [I] [Simulation] Update(): Time: 44.0 Rank: 0 StatPop: 900 Infected: 0
00:02 [0] [I] [Simulation] Update(): Time: 45.0 Rank: 0 StatPop: 900 Infected: 0
00:02 [0] [I] [Simulation] Update(): Time: 46.0 Rank: 0 StatPop: 900 Infected: 0
00:02 [0] [I] [Simulation] Update(): Time: 47.0 Rank: 0 StatPop: 900 Infected: 0
00:02 [0] [I] [Simulation] Update(): Time: 48.0 Rank: 0 StatPop: 900 Infected: 0
00:02 [0] [I] [Simulation] Update(): Time: 49.0 Rank: 0 StatPop: 900 Infected: 0
00:02 [0] [I] [Simulation] Update(): Time: 50.0 Rank: 0 StatPop: 900 Infected: 0
00:02 [0] [I] [Simulation] Finalizing 'InsetChart.json' reporter.
00:02 [0] [I] [Simulation] Finalizing 'BinnedReport.json' reporter.
00:02 [0] [I] [Simulation] Finalizing 'DemographicsSummary.json' reporter.
00:02 [0] [I] [Eradication] Controller executed successfully.

Standard error¶

When you run a simulation on an HPC cluster, it will also generate a standard error logging file (StdErr.txt) in the working directory that captures all standard error messages.

Troubleshooting EMOD simulations¶

If you encounter any of the following problems when attempting to run EMOD simulations, see the information below to resolve the issue.

If you need assistance, you can contact support for help with solving issues. You can contact Institute for Disease Modeling (IDM) support at support@idmod.org. When submitting the issue, please include any error information. See Debugging and testing for troubleshooting issues when attempting to build Eradication.exe or Eradication binary.

Contents

Exceptions ¶

Whenever EMOD encounters an error condition while running a simulation, it should throw an exception. These exceptions are designed to help you diagnose any problems so that you can quickly resolve the issue. You can find the exceptions code in the utils directory of the EMOD source code within the files Exceptions.h and Exceptions.cpp.

Each exception will return, at a minimum, the following information:

Exception type caught
The filename where the exception occurred
The line number in the file where the exception was thrown (which may not be exactly where the error actually occurred in the code)
The function name where the exception was thrown

Specific exceptions may also return additional information in a message format. This message (msg) may contain variable names or the values of those variables, the name of a file that wasn’t found, and other informational text regarding the nature of the problem. For example, a file not found exception (FileNotFoundException) might look similar to the following:

00:00:00 [0] [E] [Eradication] FileNotFoundException caught: msg = Could not find
file ./Namawala_single_node_demographics.compiled.json, filename = NodeDemographics.cpp,
lineNumber = 227

BadEnumInSwitchStatementException ¶

This exception is thrown when an enumeration value is not handled in the switch statement. In other words, this exception signals that there is a problem with an enumeration value, typically stemming from one of the configuration files, though this is not always the case.

It is possible the enumeration value is not a valid value (out of range of the numeric range of the enumeration), or perhaps the string value is currently not implemented in the code (and should not be used in configuration settings yet).

BadMapKeyException ¶

This exception is thrown when there is a standard template library (STL) mapping error. Usually, this occurs where spatial output channel names are specified in the configuration file if an unrecognized channel name is used.

If you have not modified the EMOD source code or used an unrecognized channel name, this error could signal an internal problem with the code. Contact support@idmod.org.

CalculatedValueOutOfRangeException ¶

EMOD performs a large number of mathematical operations on parameter values. Therefore, it is possible that, despite original parameter values being with range, the values resulting from these multiple calculations may end up outside its valid range. For example, a probability value (range: 0 to 1.0) that after multiple calculations during a simulation now exceeds 1.0.

This exception is thrown when such a situation is detected. This exception only applies to numeric or Boolean values, not enumeration values.

ConfigurationRangeException ¶

This exception is thrown if a parameter value read from a configuration file is detected to be outside its valid range. This exception is similar to the more general case OutOfRangeException. However, this exception is only thrown if the out of range exception comes from a numeric or Boolean value, not enumeration value, read from a configuration file.

DllLoadingException ¶

The EMOD architecture is modularized and can be built as a core Eradication.exe along with a series of custom reporter EMODules, as opposed to a single monolithic Eradication.exe. This exception indicates that EMOD couldn’t load on of the EMODule DLLs.

This situation could occur for several reasons:

EMOD couldn’t find the EMODule
Unresolved symbols were found (Windows)
EMOD could not find the necessary symbol during the GetProcAddress call
The custom reporter EMODules were built using a version of Visual Studio that is no longer supported. Rebuild the EMODules using Visual Studio 2015 (Professional, Premium, or Ultimate).

FactoryCreateFromJsonException ¶

EMOD implements class factories that instantiate objects at run time and use information from JSON- based configuration information in the creation of these objects. The exception indicates something is incorrect with the JSON information.

In particular, in some cases, the JSON information is nested into a hierarchy of information. Therefore, as the factories are called to create the objects described by the outer layers of one of these nested hierarchies, the factories do not have any knowledge yet of the inner layers of the hierarchies. This inner information contains information the factory needs to complete the object instantiation, but this information might not be correct. If that happens, then the factory will throw this exception.

Campaign files often have this kind of nested hierarchical structure, so it’s important to t verify that the hierarchy is set up correctly. For example, if the class name were mistyped and EMOD had no implementation of that class, this exception will be thrown.

FileIOException ¶

This exception is generated if there is an unrecoverable problem loading data from a file. The data might be corrupted or there may be a mismatch. For example, if the file format or configuration information indicates that there should be ten values of some array and there are only nine included in the file, then this exception would be thrown.

This exception is not the same as the exception thrown for a file that is not found. In this case, the file is found and loaded, but there is a problem with the data in the file.

FileNotFoundException ¶

This exception is thrown if a file cannot be found. Possible causes might include a incorrectly typed filename in the configuration file, a wrong path to the file, or even the path not being set in the system environment leading to the system not finding a relative path to the file. One of the most likely causes is that quotes are missing around the file name.

GeneralConfigurationException ¶

This exception is only thrown if a more specific exception cannot be used for the configuration problem detected. This exception is likely thrown when there is very little information available about the root problem.

For example, this exception might be thrown if a parameter name is invalid, such as using an older, deprecated version of a parameter name.

IllegalOperationException ¶

This exception is thrown if an illegal operation was detected. In most cases, a more specific exception will be thrown rather than this more general one. This exception is likely thrown when there is very little information available about the root problem. For example, when a utility function error is detected, there is very sparse information available as to what may have led to the error. As a result, calling a more specific exception with more context is not an option.

IncoherentConfigurationException ¶

This exception is thrown if mutually contradictory or incompatible configuration settings have been detected. For example, if mutually exclusive parameters are set, the minimum parameter value is greater than the maximum value, or two distribution axes are specified in a demographics file but there is a mismatch with the number of axes scale factors included. The exception can also occur if there isn’t a corresponding mapping between an reference ID in the metadata of a demographics file and its corresponding data file.

InitializationException ¶

This exception is thrown if a problem with initialization was detected. In most cases, a more specific exception will be thrown rather than this more general one. This exception is likely thrown when there is very little information available about the root problem.

For example, if the very first part of a JSON file has corrupted or badly formatted data, this exception may be thrown instead of the more expected file input/output exception, FileIOException.

InvalidInputDataException ¶

This exception is thrown when a problem with an input data file is detected. For example, if the wrong data type was detected, such as a float being detected when a string is expected you would see this exception thrown, or even, if a parameter has an invalid value even if the value is of the correct type. As the input data file most likely to have significant modifications, verify that the demographics file is set up correctly.

MissingParameterFromConfigurationException ¶

This exception occurs when required parameters are missing. Verify that you are not using deprecated parameters and that all required parameters are specified (or set Use_Defaults to 1).

MPIException ¶

This exception is thrown if there is an MPI error. As such, these types of issues are related to interfacing with MPI (and/or networking issues) and do not necessary imply something wrong with the EMOD code or JSON files.

NotYetImplementedException ¶

This exception is thrown if an attempt is made to execute code that is not yet implemented. For example, there are areas of EMOD where placeholder enumeration values are defined but not yet implemented. If you specify a value like this, it is considered within a valid range, but this exception will be thrown in response. Verify that any enumeration values use one of the available values as described in the documentation and do not contain any typos.

NullPointerException ¶

This exception is thrown when a NULL pointer is detected in the code, or rather when a NULL pointer - that should NOT be NULL - is used. When thrown at the application level, a NULL pointer exception is usually caused by some sort of initialization error, for example, a file not being found.

As a result, in most cases, a more specific exception will be thrown before the code execution reaches a point where this exception would occur. Therefore, this exception is uncommon and likely thrown only when there is very little information available about the root problem.

OutOfRangeException ¶

This exception is thrown when a numeric or Boolean value is out of range. For example, if you index an array outside of its valid range, this exception will be thrown. There are other situations where more specific exceptions are thrown instead of this more general one. For example, when the numeric or Boolean values are from a configuration file, but are detected to be out of range, the ConfigurationRangeException is thrown. Likewise, if the value goes out of range as the result of a calculation, the CalculatedValueOutOfRangeException is thrown instead.

QueryInterfaceException ¶

The EMOD architecture is modularized and many components now implement QueryInterface. This exception is thrown when a required interface is queried on an object and the object that returns that the interface is not supported.

If you have not modified the EMOD source code and receive this error, it could signal an internal problem with the code. Contact support@idmod.org.

SerializationException ¶

This exception is thrown when there is a serialization (or de-serialization) issue. For example, if data is being passed over the network (MPI) and the connection drops, then the serialization fails and this exception is thrown.

CentOS on Azure environment ¶

The following problems are specific to running simulations using the Eradication binary on CentOS 7.1 on Azure.

Regression test graphs differ when run on CentOS on Azure ¶

After you run regression simulations on CentOS on Azure using runemod.sh in the Scripts directory, it plots graphs from the simulation output data with a red line for the reference output and a blue line for the new output. The reference output was created by running the simulation on Windows, which in some cases may be slightly different than the output from CentOS on Azure.

For simulations that plot a baseline, you can override the Windows reference output by modifying runemod.sh to use output/InsetChart.linux.json as the output location. In that case, the red reference plots should always be completely covered by the blue plots.

Eradication binary cannot locate the input data files ¶

If you chose not to have the PrepareLinuxEnvironment.sh script download the EMOD source code and input data files, you need to set up the environment variable, path and symlink that are needed to run simulations on CentOS on Azure. See Install EMOD on CentOS on Azure.

Glossary¶

The Epidemiological MODeling software (EMOD) glossary is divided into the following subsections that define terms related to software usage, general epidemiology, and the particular disease being modeled.

Contents

Software terms
Epidemiology terms
Tuberculosis terms

Software terms ¶

The following terms are used to describe both general computing processes and concepts and the files, features, and functionality related to running simulations with Epidemiological MODeling software (EMOD).

agent-based model

A type of simulation that models the actions and interactions of autonomous agents (both individual and collective entities such as organizations or groups).

Boost

Free, peer-reviewed, portable C++ source libraries aimed at a wide range of uses including parallel processing applications (Boost.MPI). For more information, please see the Boost website, http://www.boost.org.

boxcar function

A mathematical function that is equal to zero over the entire real line except for a single interval where it is equal to a constant.

campaign

A collection of events that use interventions to modify a simulation.

campaign event

A JSON object that determines when and where an intervention is distributed during a campaign.

campaign file

A JavaScript Object Notation (JSON) formatted file that contains the parameters that specify the distribution instructions for all interventions used in a campaign, such as diagnostic tests, the target demographic, and the timing and cost of interventions. The location of this file is specified in the configuration file with the Campaign_Filename parameter. Typically, the file name is campaign.json.

channel

A property of the simulation (for example, “Parasite Prevalence”) that is accumulated once per simulated time step and written to file, typically as an array of the accumulated values.

class factory

A function that instantiate objects at run-time and use information from JSON-based configuration information in the creation of these objects.

configuration file

A JavaScript Object Notation (JSON) formatted file that contains the parameters sufficient for initiating a simulation. It controls many different aspects of the simulation, such as population size, disease dynamics, and length of the simulation. Typically, the file name is config.json.

core

In computing, a core refers to an independent central processing unit (CPU) in the computer. Multi-core computers have more than one CPU. However, through technologies such as Hyper- Threading Technology (HTT or HT), a single physical core can actually act like two virtual or logical cores, and appear to the operating system as two processors.

demographics file

A JavaScript Object Notation (JSON) formatted file that contains the parameters that specify the demographics of a population, such as age distribution, risk, birthrate, and more. IDM provides demographics files for many geographic regions. This file is considered part of the input data files and is typically named <region>_demographics.json.

disease-specific build

A build of the EMOD executable (Eradication.exe) built using SCons without any dynamic link libraries (DLLs).

dynamic link library (DLL)

Microsoft’s implementation of a shared library, separate from the EMOD executable (Eradication.exe), that can be dynamically loaded (and unloaded when unneeded) at runtime. This loading can be explicit or implicit.

EMODule

A modular component of EMOD that are consumed and used by the EMOD executable (Eradication.exe). Under Windows, a EMODule is implemented as a dynamic link library (DLL) and, under CentOS on Azure, EMODules are currently not supported. EMODules are primarily custom reporters.

Epidemiological MODeling software (EMOD)

The modeling software from the Institute for Disease Modeling (IDM) for disease researchers and developers to investigate disease dynamics, and to assist in combating a host of infectious diseases. You may see this referred to as Disease Transmission Kernel (DTK) in the source code.

Eradication.exe

Typical (default) name for the EMOD executable (Eradication.exe), whether built using monolithic build or modular (EMODule-enabled) build.

event coordinator

A JSON object that determines who will receive a particular intervention during a campaign.

flattened file

A single campaign or configuration file created by combining a default file with one or more overlay files. Multiple files must be flattened prior to running a simulation. Configuration files are flattened to a single-depth JSON file without nesting, the format required for consumption by the EMOD executable (Eradication.exe). Separating the parameters into multiple files is primarily used for testing and experimentation.

Heterogeneous Intra-Node Transmission (HINT)

A feature for modeling person-to-person transmission of diseases in heterogeneous population segments within a node for generic simulations.

high-performance computing (HPC)

The use of parallel processing for running advanced applications efficiently, reliably, and quickly.

input data files

Fixed information used as input to a simulation. Examples of such data are population, altitude, demographics, and transportation migrations. The configurable config.json and campaign.json files do not fall into this category of static fixed input.

intervention

An object aimed at reducing the spread of a disease that is distributed either to an individual; such as a vaccine, drug, or bednet; or to a node; such as a larvicide. Additionally, initial disease outbreaks and intermediate interventions that schedule another intervention are implemented as interventions in the campaign file.

JavaScript Object Notation (JSON)

A human-readable, open standard, text-based file format for data interchange. It is typically used to represent simple data structures and associative arrays, and is language- independent.

Keyhole Markup Language (KML)

A file format used to display geographic data in an Earth browser, for example, Google Maps. The format uses an XML-based structure (tag-based structure with nested elements and attributes). Tags are case-sensitive.

Link-Time Code Generation (LTCG)

A method for the linker to optimize code (for size and/or speed) after compilation has occurred. The compiled code is turned not into actual code, but instead into an intermediate language form (IL, but not to be confused with .NET IL which has a different purpose). The LTCG then, unlike the compiler, can see the whole body of code in all object files and be able to optimize the result more effectively.

Message Passing Interface (MPI)

An interface used to pass information between computing cores in parallel simulations. One example is the migration of individuals from one geographic location to another within EMOD simulations.

microsolver

A microsolver is a type of “miniature model” that operates within the framework of EMOD to compute a particular set of parameters. Each microsolver, in effect, is creating a microsimulation in order to accurately capture the dynamics of that particular aspect of the model.

Monte Carlo

Monte Carlo methods, or Monte Carlo experiments, are a class of computational algorithms utilizing repeated random sampling to obtain numerical results. Monte Carlo simulations create probability distributions for possible outcome values, which provides a more realistic way of describing uncertainty in variables.

monolithic build

A single EMOD executable (Eradication.exe) with no DLLs that includes all components as part of Eradication.exe itself. You can still use EMODules with the monolithic build; for example, a custom reporter is a common type of EMODule. View the documentation on EMODules and emodules_map.json for more information about creation and use of EMODules.

node

A grid size that is used for modeling geographies. Within EMOD, a node is a geographic region containing simulated individuals. Individuals migrate between nodes either temporarily or permanently using mobility patterns driven by local, regional, and long- distance transportation.

node-targeted intervention

An intervention that is distributed to a geographical node rather than to a single individual. One example is larvicides, which affect all mosquitoes living and feeding within a given node.

output report

A file that is the output from an EMOD simulation. Output reports are in JSON, CSV, or binary file format. You must pass the data from an output report to graphing software if you want to visualize the output of a simulation.

overlay file

An additional configuration, campaign, or demographic file that overrides the default parameter values in the primary file. Separating the parameters into multiple files is primarily used for testing a nd experimentation. In the case of configuration and campaign files, the files can use an arbitrary hierarchical structure to organize parameters into logical groups. Configuration and campaign files must be flattened into a single file before running a simulation.

preview

Software that undergoes a shorter testing cycle in order to make it available more quickly. Previews may contain software defects that could result in unexpected behavior. Use EMOD previews at your own discretion.

regression test

A test to verify that existing EMOD functionality works with new updates, typically used to refer to one of a set of scenarios included in the EMOD bundle and located in the Regression subdirectory of the bundle. Directory names of each subdirectory in Regression describe the main regression attributes, for example, “1_Generic_Seattle_MultiNode”. Also can refer to the process of regression testing of software.

release

Software that includes new functionality, scientific tutorials leveraging new or existing functionality, and/or bug fixes that have been thoroughly tested so that any defects have been fixed before release. EMOD releases undergo full regression testing.

reporter

Functionality that extracts simulation data, aggregates it, and saves it as an output report. EMOD provides several built-in reporters for outputting data from simulations and you also have the ability to create a custom reporter.

scenario

A collection of input, configuration, and campaign files that describes a real-world example of a disease outbreak and interventions. Many scenarios are included with EMOD source installations or are available in the Regression directory of the EMOD GitHub repository. The tutorials describe how to run simulations using these scenarios to learn more about epidemiology and disease modeling.

schema

A text or JSON file that can be generated from the EMOD executable (Eradication.exe) that defines all configuration and campaign parameters.

simulation

An EMOD software execution performed by the kernel. Each simulation has an associated set of files that control the inputs, configuration, and campaign.

simulation type

The disease or disease class to model.

EMOD supports the following simulation types for modeling a variety of diseases:

Generic disease (GENERIC_SIM)
Vector-borne diseases (VECTOR_SIM)
Malaria (MALARIA_SIM)
Tuberculosis (TB_SIM)
Sexually transmitted infections (STI_SIM)
HIV (HIV_SIM)

solvers

Solvers are used to find computational solutions to problems. In simulations, they can be used, for example, to determine the time of the next simulation step, or to compute the states of a model at particular time steps.

Standard Template Library (STL)

A library that contains a set of common C++ classes (including generic algorithms and data structures) that are independent of container and implemented as templates, which enables compile-time polymorphism (often more efficient than run-time polymorphism). For more information and discussion of STL, see Wikipedia.

state transition event

A change in state (e.g. healthy to infected, undiagnosed to positive diagnosis, or birth) that may trigger a subsequent action, often an intervention. “Campaign events” should not be confused with state transition events.

time step

A discrete number of hours or days in which the “simulation states” of all “simulation objects” (interventions, infections, immune systems, or individuals) are updated in a simulation. Each time step will complete processing before launching the next one. For example, a time step would process the migration data for populations moving between nodes via rail, airline, and road. The migration of individuals between nodes is the last step of the time step after updating states.

tutorial

A set of instructions in the documentation to learn more about epidemiology and disease modeling. Tutorials are based on real-world scenarios and demonstrate the mechanics of the the model. Each tutorial consists of one or more scenarios.

working directory

The directory that contains the configuration and campaign files for a simulation. You must be in this directory when you invoke Eradication.exe at the command line to run a simulation.

Epidemiology terms ¶

The following terms are used to describe general concepts and processes in the field of epidemiology and disease modeling.

antigen: A substance that is capable of inducing a specific immune response and that evokes the production of one or more antibodies.
Clausius-Clayperon relation: A way of characterizing a transition between two phases of matter; provides a method to find a relationship between temperature and pressure along phase boundaries. Frequently used in meteorology and climatology to describe the behavior of water vapor. See Wikipedia - Clausius-Clayperon relation for more information.
deterministic: Characterized by the output being fully determined by the parameter values and the initial conditions. Given the same inputs, a deterministic model will always produce the same output.
diffusive migration: The diffusion of people in and out of nearby nodes by foot travel.
epitope: The portion of an antigen that the immune system recognizes. An epitope is also called an antigenic determinant.
Euler method: Used in mathematics and computational science, this method is a first-order numerical procedure for solving ordinary differential equations with a given initial value.
exp(: The exponential function, $e^x$, where $e$ is the number (approximately 2.718281828) such that the function $e^x$ is its own derivative. The exponential function is used to model a relationship in which a constant change in the independent variable gives the same proportional change (i.e. percentage increase or decrease) in the dependent variable. The function is often written as $exp(x)$. The graph of $y = exp(x)$ is upward-sloping and increases faster as $x$ increases.
herd immunity: The resistance to the spread of a contagious disease within a population that results if a sufficiently high proportion of individuals are immune to the disease, especially through vaccination.
incidence: The rate of new cases of a disease during a specified time period. This is a measure of the risk of contracting a disease.
Koppen-Geiger Climate Classification System: A system based on the concept that native vegetation is a good expression of climate. Thus, climate zone boundaries have been selected with vegetation distribution in mind. It combines average annual and monthly temperatures and precipitation, and the seasonality of precipitation. EMOD has several options for configuring the climate, namely air temperature, rainfall, and humidity. One option utilizes Input Files that associate geographic nodes with Koppen climate indices. The modified Koppen classification uses three letters to divide the world into five major climate regions (A, B, C, D, and E) based on average annual precipitation, average monthly precipitation, and average monthly temperature. Each category is further divided into sub-categories based on temperature and precipitation. While the Koppen system does not take such things as temperature extremes, average cloud cover, number of days with sunshine, or wind into account, it is a good representation of our earth’s climate.
loss to follow-up (LTFU): Patients who at one point were actively participating in disease treatment or clinical research, but have become lost either by error or by becoming unreachable at the point of follow-up.
ordinary differential equation (ODE): A differential equation containing one or more functions of one independent variable and its derivatives.
prevalence: The rate of all cases of a disease during a specified time period. This is a measure of how widespread a disease is.
reproductive number: In a fully susceptible population, the basic reproductive number R:sub:0is the number of new cases generated by one infectious case over the course of the infectious period. The effective reproductive number takes into account non-susceptible individuals.
routine immunization (RI): The standard practice of vaccinating the majority of susceptible people in a population against vaccine-preventable diseases.
stochastic: Characterized by having a random probability distribution that may be analyzed statistically but not predicted precisely.
subpatent: When an individual is infected but asymptomatic, so the infection is not readily detectable.
SEIR model: A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, exposed, infectious, and recovered.
SEIRS model: A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, exposed, infectious, recovered, and susceptible.
SI model: A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible and infectious.
SIR model: A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, infectious, and recovered.
SIRS model: A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, infectious, recovered, and susceptible.
SIS model: A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, infectious, and susceptible.
supplemental immunization activity (SIA): In contrast to routine immunization (RI), SIAs are large-scale operations with a goal of delivering vaccines to every household.
WAIFW matrix: A matrix of values that describes the rate of transmission between different population groups. WAIFW is an abbrevation for Who Acquires Infection From Whom.
Weibull distribution: A probability distribution often used in EMOD and that requires both a shape parameter and a scale parameter. The shape parameter governs the shape of the density function. When the shape parameter is equal to 1, it is an exponential distribution. For shape parameters above 1, it forms a unimodal (hump-shaped) density function. As the shape parameter becomes large, the function forms a sharp peak. The inverse of the shape parameter is sometimes referred to here as the “heterogeneity” of the distribution (heterogeneity = 1/shape), because it can be helpful to think about the degree of heterogeneity of draws from the distribution, especially for hump-shaped functions with heterogeneity values between 0 and 1 (i.e., shape parameters greater than 1). The scale parameter shifts the distribution from left to right. When heterogeneity is small (i.e., the shape parameter is large), the scale parameter sets the location of the sharp peak.

Tuberculosis terms¶

TBD

EMOD source code installation¶

This section describes how to install the software needed to build the EMOD executable (Eradication.exe) or Eradication binary from the EMOD source code. This is necessary if you want to modify the source code to extend the capabilities of the model beyond what’s available with the latest EMOD release. For example, you may want to model a disease that isn’t currently supported in EMOD. You can build Eradication.exe from source code on computers running Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit) or build the Eradication binary on computers running CentOS 7.1 on Azure.

After building, you should run a single simulation to verify basic functionality. We recommend the 27_Vector_Sandbox scenario in the Regression directory, which is a simple five-year vector simulation with an indoor residual spraying (IRS) campaign in the third year, executed on a single-node geography that is based on Namawala, Tanzania. This simulation generally takes a few minutes to execute.

However, you can run a simulation using any of the subdirectories under Regression, which when used with the input data files provided by IDM, contain complete sets of files for EMOD simulations. See Run simulations and Use input data files for more information.

After that, we recommend running full regression tests to further verify that EMOD is behaving as expected and that none of the source code changes inadvertently changed the EMOD functionality. See Regression testing for more information.

The EMOD executable (Eradication.exe) is tested using Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit). Windows HPC Server is used for testing remote simulations on a high-performance computing (HPC) cluster and the other Windows operating systems are used to test local simulations.

The Eradication binary is tested and supported on a CentOS 7.1 on Azure virtual machine. It has also been successfully built and run on Ubuntu, SUSE, and Arch, but has not been tested and is not supported on those Linux distributions.

Prerequisites for working with EMOD source code¶

This section describes the software packages or utilities must be installed to build the EMOD executable (Eradication.exe) or Eradication binary from source code and run regression tests.

If additional software is needed for the prerequisite software due to your specific environment, the installer for the prerequisite software should provide instructions. For example, if Microsoft MPI v8 requires additional Visual C++ redistributable packages, the installer will display that information.

Note

IDM does not provide support or guarantees for any third-party software, even software that we recommend you install. Send feedback if you encounter any issues, but any support must come from the makers of those software packages and their user communities.

Install prerequisites for building Eradication.exe¶

The following software packages are required to build the Eradication.exe from EMOD source code on Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit).

Note

You must also have already installed the required software to run EMOD simulations. If you have not, see EMOD installation.

Visual Studio¶

Purchase a license from Microsoft or use an MSDN subscription to install Visual Studio 2015 (Professional, Premium, or Ultimate). Other versions of Visual Studio are not supported.

While you can use a free copy of Visual Studio Community, IDM does not test on or support this version.
Select the Visual C++ tools for Windows desktop during installation.

Python¶

Python is required for building the disease-specific Eradication.exe and running Python scripts.

Note

EMOD does not support the current version of Python.

Install Python 2.7.11 or 2.7.12. See https://www.python.org/downloads/ for instructions.
- In the Customize Python dialog box, verify that Add python.exe to PATH is selected to add Python to the PATH environment variable on your computer.
From Control Panel, select Advanced system settings, and then click Environment Variables.
Add a new variable called IDM_PYTHON_PATH and set it to the directory where you installed Python, and then click OK.
Open a Command Prompt window and type the following to verify installation:
```
python --version
```

The Python package manager, pip, is installed as part of Python 2.7.11 or 2.7.12 and is used to install other software packages.

HPC SDK¶

Install the Microsoft HPC Pack 2012 SDK (64-bit). See https://www.microsoft.com/en-us/download/details.aspx?id=36043 for instructions.

Boost¶

Go to https://sourceforge.net/projects/boost/files/boost/1.61.0/ and select one of the compressed files.
Unpack the libraries to the location of your choice.
From Control Panel, select Advanced system settings, and then click Environment Variables.
Add a new variable called IDM_BOOST_PATH and set it to the directory where you installed Boost, and then click OK.

SCons¶

SCons is required for the building disease-specific Eradication.exe and is optional for the monolithic Eradication.exe that includes all simulation types.

Go to http://www.lfd.uci.edu/~gohlke/pythonlibs#scons and select the WHL file for SCons 2.5.0 that is compatible with Python 2.7.11 or 2.7.12.
Save the file to your Python installation directory.
Open a Command Prompt window and navigate to the Python installation directory, then enter the following, substituting the name of the specific matplotlib WHL file you downloaded:
```
pip install scons-2.x.x-py2-none-any.whl
```

Install prerequisites for building the Eradication binary¶

For CentOS 7.1 on Azure, all prerequisites for building the Eradication binary are installed by the setup script described in Install EMOD on CentOS on Azure. If you originally installed EMOD without including the source code and input data files that are optional for running simulations using a pre-built Eradication binary, rerun the script and install those.

Install prerequisites for running regression tests¶

The following plotting software is required for running regression tests, where graphs of model output are created both before and after source code changes are made to see if those changes created a discrepancy in the regression test output. For more information, see Regression testing. For CentOS 7.1 on Azure, all Python packages are installed by the setup script, but you may want to install R or MATLAB.

We recommended that you download some of the Python packages from http://www.lfd.uci.edu/~gohlke/pythonlibs, a page compiled by Christoph Gohlke, University of California, Irvine. The libraries there are compiled Windows binaries, including the 64-bit versions required by EMOD.

Python utilities¶

The Python utilities dateutil, six, and pyparsing provide text parsing and datetime functionality.

Open a Command Prompt window.

Enter the following commands:

pip install python-dateutil
pip install pyparsing

NumPy¶

The NumPy package adds support for large, multi-dimensional arrays and matrices to Python.

Go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy and select the WHL file for NumPy 1.11.3 (64-bit) that is compatible with Python 2.7.11 or 2.7.12.
Save the file to your Python installation directory.
Open a Command Prompt window and navigate to the Python installation directory, then enter the following, substituting the name of the specific NumPy WHL file you downloaded:
```
pip install numpy-1.x.x+mkl-cp27m-win_amd64.whl
```

Matplotlib¶

Matplotlib is a Python library for making publication quality plots using syntax familiar to MATLAB users. Matplotlib also uses NumpPy for numeric manipulation. Output formats include PDF, Postscript, SVG, and PNG, as well as a screen display.

Go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#matplotlib and select the WHL file for Matplotlib 1.5.3 (64-bit) that is compatible with Python 2.7.11 or 2.7.12.
Save the file to your Python installation directory.
Open a Command Prompt window and navigate to the Python installation directory, then enter the following, substituting the name of the specific matplotlib WHL file you downloaded:
```
pip install matplotlib-1.x.x+mkl-cp27m-win_amd64.whl
```

R¶

The IDM test team uses R 3.2.0 (64-bit) for regression testing, but it is considered optional.

R is a free software environment for statistical computing and graphics.

Go to https://www.r-project.org/ and install R 3.2.0 (64-bit).

MATLAB¶

The IDM test team uses MATLAB R2015a and the MATLAB Statistics and Machine Learning Toolbox™ R2015a for regression testing, but they are both considered optional.

MATLAB is a high-level language and interactive environment for numerical computation, visualization, and programming. The MATLAB Statistics and Machine Learning Toolbox™ provides functions and applications to describe, analyze and model data using statistics and machine learning algorithms.

Go to http://www.mathworks.com/products/matlab/ and install MATLAB R2015a.
If desired, go to https://www.mathworks.com/products/statistics.html and install the MATLAB Statistics and Machine Learning Toolbox™ R2015a.

Download the EMOD source code¶

The EMOD source code is available on GitHub. The EMOD source includes the source code, Visual Studio solution, sample configuration files, as well as regression test and other files needed to fully build and test the EMOD executable (Eradication.exe).

You can have multiple versions of the EMOD source code in separate directories on your local computer. For example, you might want to download a new release of EMOD but also keep a previous release of the source. In the following examples, the source code is downloaded to the directory EMOD at C:/IDM, but you can save to any location you want.

You can use a Git client of your choice to download from GitHub, however, these instructions show how to use Git GUI and Git Bash.

Install Git GUI and Git Bash¶

To install Git GUI and Git Bash, download a 64-bit version of Git from https://git-scm.com/download. On the Select Components installer window, you can select one or both of Git GUI Here for a GUI or Git Bash Here for a command window.

Use Git GUI to download the EMOD source¶

Launch the Git GUI application and click Clone Existing Repository.
From the Clone Existing Repository window:
1. In Source Location, enter https://github.com/InstituteforDiseaseModeling/EMOD
2. In Target Directory, enter the location and target directory name: C:/IDM/EMOD
3. Click Clone. Git GUI will create the directory and download the source code.
Close the Git GUI window when the download completes.

Use Git Bash to download the EMOD source¶

Note

For a list of the Git Bash commands, you can type git help git from Git Bash, or git help <command> for information about a specific command.

Launch the Git Bash application. From the command line:
1. Go to the location where you want your copy of the EMOD source located:
```
cd C:\IDM
```
2. Clone the repository from GitHub:
```
git clone https://github.com/InstituteforDiseaseModeling/EMOD
```

Git Bash will copy the EMOD source to a directory named EMOD by default as a different directory name was not specified after the URL.

Verify that all directories on https://github.com/InstituteforDiseaseModeling/EMOD are now reflected on your local clone of the repository. The table below describes each of the directories.

Directory	Description
Dependencies	The ComputeClusterPack.
Eradication	Eradication project. Main project for Visual Studio solution.
Regression	Scripts to perform regression tests for modified code. It also includes a copy of the Scenarios directory that contains simulation configuration, batch, input, and script files that are associated with tutorials.
Scripts	Scripts used in file creation.
UnitTest++	Software for early release of additional regression testing.
baseReportLib	Static library of classes used by EMOD built-in reports and custom reports.
cajun	Cajun C++ API for JavaScript Object Notation (JSON) project.
campaign	Campaign project.
componentTests	Tests that work in conjunction with the UnitTest++ directory.
interventions	Interventions project.
rapidjson	Rapid JSON project.
reporters	Collection of custom reporter projects.
snappy	Compression utilities used by the EMOD software.
utils	Miscellaneous utilities.

Additionally, the repository includes additional files, such as the Visual Studio solution file, SCons scripts, the README, and more.

Build EMOD from source code¶

You can build the Eradication.exe for Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit) using Microsoft Visual Studio or SCons. You can build the Eradication binary for CentOS 7.1 on Azure using SCons.

If you want full debugging support, you must build using Visual Studio. However, Visual Studio is only capable of a monolithic build that includes all supported simulation types.

EMOD supports the following simulation types for modeling a variety of diseases:

Generic disease (GENERIC_SIM)
Vector-borne diseases (VECTOR_SIM)
Malaria (MALARIA_SIM)
Tuberculosis (TB_SIM)
Sexually transmitted infections (STI_SIM)
HIV (HIV_SIM)

If you want to create a disease-specific build, you must build using SCons. However, SCons builds build only the release version without extensive debugging information.

Build a monolithic Eradication.exe with Visual Studio¶

You can use the Microsoft Visual Studio solution file in the EMOD source code repository to build the monolithic version of the EMOD executable (Eradication.exe), which can be either a release or debug build. Visual Studio 2015 (Professional, Premium, or Ultimate) is the currently supported version.

Warning

Visual Studio creates a debug build by default. However, you must use a release build to commission simulations to COMputational Platform Service (COMPS); attempting to use a debug build will result in an error.

Open the EradicationKernel solution in Visual Studio.
From the Solution Configurations ribbon, select Debug or Release.
On the Build menu, select Build Solution.

Eradication.exe will be in a subdirectory of the Eradication directory.

Build Eradication.exe or Eradication binary with SCons¶

SCons is a software construction tool that is an alternative to the well-known “Make” build tool. It is implemented in Python and the SCons configuration files, SConstruct and SConscript, are executed as Python scripts. This allows for greater flexibility and extensibility, such as the creation of custom SCons abilities just for EMOD. For more information on Scons, see www.scons.org. SCons 2.5.0 is the currently supported version.

Warning

EMOD will not build if you use the --Debug flag. To build a debug version, you must use Visual Studio.

Open a command window.
Go to the directory where EMOD is installed:
```
cd C:\IDM\EMOD
```
Run the following command to build Eradication.exe:
- For a monolithic build:
```
scons --Release
```
- For a disease-specific build, specify one of the supported disease types using the --Disease flag:
```
scons --Release --Disease=Vector
```
The executable will be placed, by default, in the subdirectory build\x64\Release\Eradication\ of your local EMOD source.

Additionally, you can parallelize the build process with the --jobs flag. This flag indicates the number of jobs that can be run at the same time (a single core can only run one job at a time). For example:

scons --Release --Disease=Vector --jobs=4

EMOD architecture¶

Epidemiological MODeling software (EMOD) is implemented in C++ and has two subsystems: the environment and the simulation controller. the environment contains the interfaces to the simulation controller subsystem and the external inputs and outputs. the simulation controller contains the epidemiological model (simulation and campaign management), and reporters that capture output data and create reports from a simulation. The following figure shows a high-level view of the system components of EMOD and how they are related to each other.

EMOD system components¶

Warning

If you modify the source code to add or remove configuration or campaign parameters, you may need to update the code used to produce the schema. You must also verify that your simulations are still scientifically valid.

Environment subsystem¶

The environment subsystem provides access to global resources and most of the external input and output interfaces. Output reports are the only output not handled through the environment subsystem. It consists of a a singleton utility class, Environment. The environment subsystem consists of five logical components.

The following figure shows a high-level view of the system components of EMOD and how they are related to each other.

EMOD system components¶

Input file readers¶

Provide small utility functions for reading in the input data, configuration, and campaign files. The actual parsing of the configuration and campaign files is done by the configuration manager component. The parsing of the input data files is done by the model classes that consume that data, which are part of the simulation controller subsystem.

Configuration manager¶

Parses the configuration files, stores the parsed values for system-global configuration values, and provides a central container resource for the configuration as JSON to those classes that need to parse out the remaining configuration values locally for themselves. It retains the contents of the configuration file and the directories provided on the command line.

Error handler¶

Provides an application-level exception library and a centralized mechanism for handling all errors that occur during program execution. These errors are ultimately reported through the logging component.

Logger¶

Writes the output logs and errors. Output reports, such as time-series channel data reports, spatial channel data reports, progress and status summary, and custom reports, are handled by the simulation controller. For information and setting the logging level, see Set log levels.

Message Passing Interface (MPI)¶

EMOD is designed to support large-scale multi-node simulations by running multiple instances of EMOD in parallel across multiple compute nodes on a cluster. During initialization, geographical nodes are assigned to compute nodes. Individuals who migrate between geographical nodes that are not on the same compute node are migrated via a process of serialization, network I/O, and deserialization. The network communication is handled through a mixture of direct calls to the MPI library and use of Boost’s MPI facilities. This component provides the system-wide single interface to MPI, caching the number of tasks and rank of current process from the MPI environment.

Simulation controller subsystem¶

The simulation controller is the top-level structure for the epidemiological model. The controller’s capabilities are simple, running a single simulation in a single time direction at a constant rate. It exists to support future capabilities, such as running multiple simulations, pausing a simulation, or bootstrapping a simulation from an archived simulation. It contains two components: simulation and reporters.

The following figure shows a high-level view of the system components of EMOD and how they are related to each other.

EMOD system components¶

Simulation¶

The simulation component contains core functionality that models the behavior of a disease without any interventions and extended functionality to include migration, climate, or other input data to create a more realistic simulation. Disease transmission may be more or less complex depending on the disease being modeled.

Campaign management¶

The simulation component also includes a campaign manager sub-component for including disease interventions in a simulation.

Reporter¶

The reporter component creates output reports for both simulation-wide aggregate reporting and spatial reporting.

Simulation core components¶

The simulation component contains core functionality that models the behavior of a disease without any interventions and extended functionality to include migration, climate, or other input data to create a more realistic simulation. Disease transmission may be more or less complex depending on the disease being modeled.

Warning

If you modify the source code to add or remove configuration or campaign parameters, you may need to update the code used to produce the schema. You must also verify that your simulations are still scientifically valid.

Content

Simulation
Node
IndividualHuman
- Disease transmission

Each generic EMOD simulation contains the following core base classes:

Simulation: Created by the simulation controller via a SimulationFactory with each run of EMOD.
Node: Corresponds to a geographic area. Each simulation maintains a collection of one or more nodes.
IndividualHuman: Represents a human being. Creates Susceptibility and Infection objects for the collection of individuals it maintains. The file name that defines this class is “Individual” and you may see it likewise shortened in diagrams.
Susceptibility: Manages an individual’s immunity.
Infection: Represents an individual’s infection with a disease.

For generic simulations, human-to-human transmission uses a homogeneous contagion pool for each node. Every individual in the node sheds disease into the pool and acquires disease from the pool. For specific disease simulations, transmission is heterogeneous and based on the disease biology.

The relationship between these classes is captured in the following figure.

Simulation components¶

After the simulation is initialized, all objects in the simulation are updated at each time step, typically a single day. Each object implements a method Update that advances the state of the objects it contains, as follows:

Controller updates Simulation
Simulation updates Nodes
Node updates IndividualHuman
IndividualHuman updates Susceptibility, Infections, and InterventionsContainer
InterventionsContainer updates Interventions

Simulation ¶

As a stochastic model, EMOD uses a random number seed for all simulations. The Simulation object has a data member (RNG) that is an object maintaining the state of the random number generator for the parent Simulation object. The only generator currently supported is pseudo-DES. The random seed is initialized from the configuration parameter Run_Number and from the process MPI rank. All child objects needing access to the RNG must be provided an appropriate (context) pointer by their owners.

The Simulation class contains the following methods:

Method

Description

Populate()

Initializes the simulation. The Populate method initializes the simulation using both the configuration file and the demographic files. Populate calls through to populateFromDemographics to enable the Simulation object to create one or many Node objects populated with IndividualHumans as dictated by the demographics file, in conjunction with the sampling mode and value dictated by the configuration file. If the configuration file indicates that a migration and a climate model are to be used, those input file are also read. Populate also initializes all Reporters.

Update()

Advances the state of nodes.

Simulation object hierarchy¶

For multi-core parallelization, the demographics file is read in order on each process and identity of each node and is compared with a policy assigning nodes to processes embodied in objects implementing InitialLoadBalancingScheme. If the initial load balancing scheme allows a node for the current rank, the node is created via addNewNodeFromDemographics. After all nodes have been created and propagated, the NodeRankMaps are merged across all processes. See Load-balancing file structure for more information.

Node ¶

Nodes are model abstractions that represent a population of individuals that interact in a way that does not depend on their geographic location. However, they represent a geographic location with latitude and longitude coordinates, climate information, migration links to other nodes, and miscellaneous demographic information. The Node is always the container for IndividualHumans and the contagion pool. The Node provides important capabilities for how IndividualHumans are created and managed. It can also contain a Climate object and Migration links if those features are enabled. The climate and migration settings are initialized based on the information in the input data files.

The Node class contains the following methods:

Method	Description
PopulateFromDemographics()	The entry point that invokes populateNewIndividualsFromDemographics(InitPop), which adds individuals to a simulation and initializes them. PopulateNewIndividualsFromBirth() operates similarly, but can use different distributions for demographics and initial infections.
Update()	Advances the state of individuals.
updateInfectivity()	The workhorse of the simulation, it processes the list of all individuals attached to the Node object and updates the force of infection data members in the contagion pool object. It calls a base class function updatePopulationStatistics, which processes all individuals, sets the counters for prevalence reporting, and calls IndividualHuman::GetInfectiousness for all IndividualHuman objects. The code in GetInfectiousness governs the interaction of the IndividualHuman with the contagion pool object. The rest of the code in updateInfectivity processes the contagion beyond individual contributions. This can include decay of persisting contagion, vector population dynamics, seasonality, etc. This is also where the population-summed infectivity must be scaled by the population in the case of density-independent transmission.
updateVitalDynamics()	Manages community level vital dynamics, primarily births, since deaths occur at the individual level.

By default, an IndividualHuman object is created, tracked, and updated for every person within a node. To reduce memory usage and processing time, you may want to sample such that each IndividualHuman object represents multiple people. There are several different sampling strategies implemented, with different strategies better suited for different simulations. See Sampling for more information.

If migration is enabled, at the end of the Node update, the Node moves all migrating individuals to a separate migration queue for processing. Once the full simulation time step is completed, all migrating individuals are moved from the migration queue and added to their destination nodes.

IndividualHuman ¶

The IndividualHuman class corresponds to human beings within the simulation. Individuals are always contained by a Node object. Each IndividualHuman object may represent one or more human beings, depending on the sampling strategy and value chosen.

The IndividualHuman class contains components for susceptibility, infection, and interventions. Infection and Susceptibility cooperate to represent the detailed dynamics of infection and immune mechanisms. Every IndividualHuman contains a Susceptibility object that represents the state of the immune system over time. Only an infected IndividualHuman contains an Infection object, and may contain multiple Infection objects.. Susceptibility is passed to initialize the infection immunology in Infection::InitInfectionImmunology(). The state of an individual’s susceptibility and infection are updated with Update() methods. Disease-specific models have additional derived classes with properties and methods to represent specifics of the disease biology.

The InterventionsContainer is the mediating structure for how interventions interrupt disease transmission or progression. Campaign distribution results in an Intervention object being added to an individual’s InterventionsContainer, where it remains unless and until it is removed. When an IndividualHuman calls Update(), the InterventionsContainer is updated and its effects are applied to the IndividualHuman. These effects are used in the individual, infection, and susceptibility update operations. If migration is enabled, at the end of each individual’s update step EMOD checks if the individual is scheduled for migration (IndividualHuman::CheckForMigration()), setting a flag accordingly.

The IndividualHuman class contains the following methods:

Method	Description
Update()	Advances the state of both the infection and the immune system and then registers any necessary changes in an individual’s state resulting from those dynamics (that is, death, paralysis, or clearance). It also updates intrinsic vital dynamics, intervention effects, migration, and exposure to infectivity of the appropriate social network.
ExposeToInfectivity()	Passes the IndividualHuman to the ExposeIndividual() function if it is exposed to infectivity at a time step.
UpdateInfectiousness()	Advances the quantity of contagion deposited to the contagion pool by an IndividualHuman at each time step of their infectious period. This is explained in more detail below.

Disease transmission ¶

Transmission of disease is mediated through a pool mechanism which tracks abstract quantities of contagion. The pool mediates individual acquisition and transmission of infections as well as external processes that modify the infectivity dynamics external to individuals. The pool provides basic mechanisms for depositing, decaying, and querying quantities of contagion which are associated with a specific StrainIdentity. The pool internally manages a separate ContagionPopulation for each possible antigen identity. ContagionPopulations have further structure and manage an array of contagion quantities for each substrain identity.

Each IndividualHuman has a sampling weight $W_i$ and a total infectiousness $X_i$, the rate at which contacts with the infectious individual become infected. This rate can be modified by transmission-reducing immunity or heterogeneous contact rates, which are gathered in $Y_{i,transmit}$, and the transmission-reducing effects of interventions, such as transmission- blocking vaccines in the factor $Z_{i,transmit}$. The sampling weight $W_i$ is not included in the probability of acquiring a new infection. Sample particles are simulated as single individuals, their weighting $W_i$ is used to determine their effects upon the rest of the simulation.The total infectiousness $T$ of the local human population is then calculated as:

\[T = \sum_iW_iX_iY_{i,transmit}Z_{i,transmit}\]

For simulation of population density-independent transmission, individual infectiousness $X_i$ includes the average contact rate of the infectious individual, so this total infectiousness is divided by the population $P$ to get the force of infection $FI=\frac{T}{P}$ for each individual in the population. The base rate of acquisition of new infections per person is then $FI$, which can be modified for each individual $I$ by their characteristics $Y_{i,acquire}$ and interventions $Z_{i,acquire}$. Over a time step $\Delta t$, the probability of an individual acquiring a new infection is then:

\[P_{I,i} = 1- \text{exp}(-F_IY_{i,acquire}Z_{i,acquire}\Delta t)\]

A new infection receives an incubation period and infectious period from the input configuration (a constant at present, but possibly from specified distributions in the future) and a counter tracks the full latency, which is possible when simulating individual particles. After the incubation period, the counter for the infectious period begins, during which time the infection contributes to the individual’s infectiousness $X_i$.

Campaign management¶

Campaigns are structured into campaign events that determine when, where, and to whom the intervention will be distributed and interventions that determine what is distributed, for example vaccines or other treatments. This section describes the software architecture used for managing campaigns.

Campaigns can be very simple, such as a single intervention with fixed parameters at a point in time, or a complex, adaptive, repetitive, and responsive combination of interventions. A Campaign is a collection of events that modify a simulation. An Event is a distribution within a campaign, and an Intervention is an item that is given out, such as a vaccine, a drug or a bednet.

Warning

If you modify the source code to add or remove configuration or campaign parameters, you may need to update the code used to produce the schema. You must also verify that your simulations are still scientifically valid.

Contents

Campaign events
Interventions
- Individual-distributed interventions
- Node-distributed interventions

Campaign events ¶

A campaign event triggers the campaign management system to evaluate when, where, and to whom the intervention will be distributed. This section describes the SimulationEventContext, CampaignEvent, NodeSet, and EventCoordinator classes used to manage this process.

For more information on setting up campaign files, see Create a campaign file.

SimulationEventContext ¶

The Simulation class has a nested helper class called SimulationEventContextHost, referred to as SimulationEventContext. It serves as the interface to, or manager of, CampaignEvents for the Simulation class.

Incoming: The Simulation to SimulationEventContext interface contains the following methods:

Method	Description
LoadCampaignFromFile()	Loads the campaign file during simulation initialization (t=0).
RegisterEventCoordinator()	Registers new event coordinators. Also used by CampaignEvent during activation to notify the SimulationEventContext of a new active EventCoordinator.
Update()	Advances the state at each time step.
VisitNodes()	Called from CampaignEvent during its activation. CampaignEvent is not a Node container, so it needs to communicate with the Simulation class to do anything with nodes.

Outgoing: The SimulationEventContext communicates with CampaignEvents and EventCoordinators, which are described below.

CampaignEvent ¶

The CampaignEvent class exists primarily as an initialization-time, minimal intelligence container with a 1-to-1 mapping for CampaignEvents found in the JSON campaign file. They do not do much in the way of run-time campaign distribution or control.

Incoming: The public interface for CampaignEvent contains the following methods:

Method

Description

CreateInstance()

For each campaign event that the SimulationEventContext finds in the campaign file, it creates a corresponding CampaignEvent instance via CampaignEventFactory. The nested JSON elements in a campaign event are stored as an opaque (that is, unparsed) JSON object known as Event_Coordinator_Config. Immediate parameters like Start_Day are parsed out of the JSON and stored in explicit variables in the CampaignEvent class.

Dispatch()

In its steady-state, the SimulationEventContext checks the start day for all existing CampaignEvents and invokes this method to when the time step matches the start day.

Outgoing: The CampaignEvent communicates with two other classes: NodeSet and EventCoordinator.

NodeSet ¶

The NodeSet class parses the Nodeset_Config and helps the EventCoordinator determine if a given node is included in a particular intervention distribution. It can be as simple as NodeSetAll, which includes all nodes, or something much more involved. Usual cases include NodeSetPolygon and NodeSetCommunityList.

Incoming: The public interface for NodeSet contains the following methods:

Method	Description
Contains()	The caller passes a Node via the INodeEventContext interface and gets back a Boolean. The NodeSet class completely encapsulates whatever logic is used to evaluate node membership.

EventCoordinator ¶

EventCoordinator is a base type that contains most of the functionality in the event distribution mechanism. There are two actual EventCoordinator classes implemented, but you can new implementations easily by creating a new EventCoordinator class.

Incoming: There are four classes or class groups that make calls into EventCoordinator:

CampaignEvents, which create and activate them.
The SimulationEventContext that invokes their steady-state methods.
Nodes (via the NodeEventContext helper class)
Interventions, which get EventCoordinator-level configuration data needed for intervention execution.

The public interface for EventCoordinator contains the following methods:

Method	Description
AddNode()	Invoked by CampaignEvent::Dispatch() to allow the EventCoordinator to act as a node container and have access to all its constituent nodes via the INodeEventContext interface. Note The AddNode calls actually come via a delegate function that is in the EventCoordinator, but is invoked from the VisitNodes method up in the SimulationEventContext. The CampaignEvent passes the delegate function it wants used when it calls SimulationEventContext::VisitNodes() . This circuitous journey is needed because EMOD operates on a node-by-node basis and the SimulationEventContext, not the CampaignEvent, is the primary node container.
CreateInstance()	During their instantiation, CampaignEvents create EventCoordinators as the CampaignEvents parse the Event_Coordinator_Config, which specifies the class name and is passed by SimulationEventContext. Nothing then happens until CampaignEvent::Dispatch() is called.
IsFinished()	When an EventCoordinator determines that it is finished distributing its interventions, it sets an internal flag. The SimulationEventContext queries the IsFinished() function on each registered EventCoordinator and disposes of it if true. An EventCoordinator can activate and complete in a single time step, or it could last the entire length of the simulation.
Update()	The SimulationEventContext calls this on all registered EventCoordinators to advance their state at each time step. This method exists for global EventCoordinator communication and logic.
UpdateNodes()	Distributes the actual intervention. For the simplest possible use case, an EventCoordinator will distribute an intervention on its start day and then finish in that same time step. However, an EventCoordinator may implement repeating interventions, phased distributions, or sustained distributions that are contingent on node or individual attributes.

Outgoing: The outgoing function calls consist of the distribution of the intervention either to individuals or nodes. For the interface between the EventCoordinator and the actual Intervention, the EventCoordinator calls InterventionFactory and passes it the remaining unparsed JSON from the campaign file. Namely, this is the Intervention_Config, the actual intervention-level campaign parameters including the intervention class. The InterventionFactory returns the newly created intervention instance for each individual via a one of two types of IDistributableIntervention interfaces.

Interventions ¶

Interventions are the actual objects aimed at reducing the spread of disease, such as a vaccine or drug. THey can be either distributed to individuals or to nodes. For example, a vaccine is an individual-distributed intervention and a larvicide is a node-distributed intervention. Intervention is an abstract type class and specific interventions are classes that implement either IDistributableIntervention, for individual-distributed interventions, or INodeDistributableIntervention, for node-distributed interventions.

The determination of whether an intervention is individual-distributed or node-distributed is contained by logic in InterventionFactory. At the beginning of the EventCoordinator::UpdateNodes()**method, the **InterventionFactory is queried with the unparsed JSON campaign file to see if it returns an INodeDistributableInterface pointer. If it does, the intervention is executed as a node-distributed intervention; if it does not, the individual- distributed intervention execution is used.

Incoming: The interface to the abstract type Intervention class is the Distribute() method. It functions slightly differently for each intervention type.

Outgoing: Intervention is an abstract type that interacts with Nodes or IndividualHumans with an interface specific to that intervention type. The intervention calls QueryInterface on the container to ask for a consumer interface, for example IBednetConsumer. If supported, a Give method specific to that intervention will be called, for example, GiveBednet(this) to give itself to this individual. It is then up to the internal IndividualHuman or Node logic to decide what to do with this intervention.

Individual-distributed interventions ¶

An individual-distributed intervention implements IDistributableIntervention, a container for the actual parameter name-value pairs from the campaign file, that is contained by an IndividualContainer, an IndividualHuman helper class. The EventCoordinator calls IDistributableIntervention::Distribute(), passing the pointer IIndividualHumanDistributionContext, which provides an ISupports interface to the individual by which the intervention can request a consumer interface. This interface can then be used with Give().

In the EventCoordinator::UpdateNodes() method, the EventCoordinator organizes the distribution of Interventions to IndividualHumans mediated by Nodes, which contain the IndividualHuman objects. The EventCoordinator also applies campaign parameters at the event coordinator level (for example, Target_Demographic, Demographic_Coverage, Num_Repetitions, and Days_Between_Reps) which mainly consists of filtering the distribution to individuals based on individual attributes.

Node-distributed interventions ¶

A node-distributed intervention implements INodeDistributableIntervention, a container for the actual parameter name-value pairs from the campaign file, that is contained by a Node. The EventCoordinator calls INodeDistributableIntervention::Distribute(), passing the pointer INodeEventContext, which provides an ISupports interface to the individual by which the intervention can request a consumer interface. This interface can then be used with Give(). Similar to individual-distributed interventions, the EventCoordinator also applies campaign parameters for filtering the distribution.

The architectural diagram below illustrates how campaign file settings are processed by an EMOD simulation.

EMOD campaign architecture¶

Reporter component¶

After running simulations, EMOD creates output reports that contain the model results. Two methods of coordinated reporting are implemented: simulation-wide aggregated reporting and spatial reporting. See Output report structure for more information about the different output reports available.

Simulation-wide aggregate¶

Simulation-wide aggregate reporting is the most commonly used. This reporting method generates the output written to InsetChart.json. Conceptually, for each time step, the value of a named channel is derived from values provided for that channel by each node. The values are accumulated (summed) over all nodes and then transformed (often normalized by an internal parameter or another channel) just prior to writing. This is implemented through the simulation’s insetChartDataAccumulator.

Basic usage of the NodeTimestepDataAccumulator is explained in Report.h. The simulation is responsible for calling Begin/EndTimestep() and collecting and writing out the data at the end of the simulation. The accumulation calls occur in Node::accumulateInsetChartData().

Spatial¶

The second method is spatial reporting which is facilitated by SpatialReporter. In spatial reporting, each node again produces values for named channels, but no simulation-wide accumulation takes place. Instead, the values of each channel for each node are written to a binary table (ReportNNNN.dat) where NNNN is the time step. The format of this file is simple and can be summarized as the maximally packed layout of this structure:

struct ReportDataFormat
{
    int32_t num_nodes;
    int32_t num_channels;
    float data[num_nodes*num_channels];
}

Num_Channels is the number of user channels implicitly generated by the number of different channel names requested in calls to Node::reportSpatialDataReportChannels() plus two additional channels for longitude and latitude. JSON metadata for the ReportNNNN.dat files is written to Report.header.dat after the first time step. It does not include entries for latitude and longitude.

In general, the value of channel $c$ for node $n$, starting at zero, is found at $data[n\times num\_channels+c]$. The latitude is $data[n\times num\_channels+0]$ and the longitude is $data[n\times num\_channels+1]$.

The report data files are written after every time step at the request of the Controller by calling WriteTimestep(). Under MPI, the default implementation reduces all the data to rank 0 and writes the combined data out to file on rank 0.

See Custom reporters for information about how to use a custom reporter.

Debugging and testing¶

When you build Eradication.exe or Eradication binary from the EMOD source code, it’s important to debug the code and run regression tests to be sure your changes didn’t inadvertently change EMOD functionality. You can run simulations directly from Visual Studio to step through the code, troubleshoot any build errors you encounter, and run the standard IDM regression tests or create a new regression test.

Warning

If you modify the source code to add or remove configuration or campaign parameters, you may need to update the code used to produce the schema. You must also verify that your simulations are still scientifically valid.

Set log levels¶

EMOD simulations output several files, including log files. See Error and logging files for more information on the logging files that are created by a simulation. By default, logging is at the “INFO” level, but you can set the level at which you want logging information generated. If you see redundant log messages, you can also throttle logging to eliminate them.

There are five log levels. The level chosen will log messages for that level and all higher levels (levels of smaller numeric value). You can set default log levels across the entire Eradication.exe or for individual files in the Eradication.exe; individual file settings will override the default. For example, if you want to increase logging for a particular file you are debugging without increasing logging across the entire Eradication.exe, you can set the default log level to “WARNING” and set the log level for the file to “DEBUG”. The standard output will reflect the default log level for all settings except “ERROR” or “WARNING”. For example:

Log-levels:
        Default -> INFO
        Eradication -> INFO

The following log levels are available:

ERROR (1): This is the highest level. Only errors, including critical errors, are logged to standard output.
WARNING (2): Warnings and errors are logged to standard output.
INFO (3): This is the default logging level for the executable and is set implicitly. Informational messages, warnings, and errors are logged to standard output.
DEBUG (4): Debug information, informational messages, warnings, and errors are logged to standard output. Debug level logging messages do not require a debug build of Eradication.exe. This setting will generate a large number of messages and may impact performance. If you need the debugging messages, we recommend that you limit the number of files with this setting and not set it as the default across the entire Eradication.exe.
VALID (5): This is the lowest level. Validation information, debug information, informational messages, warnings, and errors are logged to standard output. This log level is for very low-level debugging of specific values, including values in tight loops. Because it could severely impact performance, this level will only output to standard output during a debug build of Eradication.exe.

Set default Eradication.exe level¶

To set the default log level across the entire Eradication.exe, add the parameter logLevel_default to your configuration file and set it to the log level desired. For example:

{
    "logLevel_default": "WARNING"
}

Set level for individual files¶

You can set the log level for an individual file, if it supports the ability. This is typically only used for debugging after making source code changes. This setting will override the default set using logLevel_default, whether set to a higher or lower level.

In the C++ files that make up the EMOD source code, open the file for which you want to set the log level.
Search for static const char * _module.

“Module” refers to the C++ file and is not the same as an EMODule. If this string isn’t present, you cannot set a log level for the file.
Look for logging macros in the file, such as LOG_WARN(x).

If macros are present, this indicates that the file supports that log level setting. The file may not support all log levels. Logging macros can be found in utils/Log.h. You can also add your own logging messages to the file using the logging macros.
In the configuration file, add the logLevel_<FileName> parameter with the log level desired. For example:
```
{
    "logLevel_SimulationVectorExport":"DEBUG"
}
```

Throttle redundant logging¶

It’s possible that multiple identical logging messages are generated by a file of Eradication.exe. This is normal behavior, but the verbosity can be unwanted or unnecessary. You can eliminate duplicate messages by turning on “throttling”, which retains only one logging message instead of a series of duplicate messages from each file.

Only one copy of the message will be generated when it is one of a series of duplicates from the same file. The identical messages need not be output one right after one another to be throttled. If an identical message is output from a different file, it will still be generated. Throttling can only be enabled across the entire Eradication.exe, not per file. It is off by default.

To enable throttling, add the following parameter and value to your configuration file:

{
    "Enable_Log_Throttling": 1
}

For example, the following log messages are seen with throttling turned off:

00:00 [0] [D] [FileA] identical message: I'm in FileA
00:00 [0] [D] [FileB] another message from B
00:00 [0] [D] [FileA] identical message: I'm in FileA
00:00 [0] [D] [FileB] different message from B
00:00 [0] [D] [FileA] identical message: I'm in FileA
00:00 [0] [D] [FileB] yet another message from B.
00:00 [0] [D] [FileA] identical message: I'm in FileA

With throttling on, the repeated messages from file A are removed, even though they are intermixed with other log messages from file B:

00:00 [0] [D] [FileA] identical message: I'm in FileA
00:00 [0] [D] [FileB] another message from B
00:00 [0] [D] [FileB] different message from B
00:00 [0] [D] [FileB] yet another message from B.

Run debug simulations in Visual Studio¶

If you have modified the EMOD source code, you can run simulations directly from Visual Studio as part of debugging your changes by using the built-in debugger. For example, you may want to run one or more of the simulations in the Regression directory to verify that the results match those obtained prior to your changes.

Open the EradicationKernel solution in Visual Studio.
On the Solution Explorer pane, right-click the Eradication project and select Properties.
On the Property Pages window, in the Configuration Properties pane, click Debugging.
Set the Command Arguments and Working Directory to the appropriate values and click OK. See Run a simulation using the command line for more information.
On the Debug menu, do one of the following:
- Click Start Without Debugging to run the simulation in Release mode, where the simulation runs with essentially the same behavior and performance as running it at the command line.
- Click Start Debugging to run the simulation in Debug mode, where you have the ability to add break points and step through the code, inspecting the values of different variables throughout the simulation.

Troubleshooting EMOD builds¶

If you encounter any of the following warnings or errors when attempting to build the EMOD executable (Eradication.exe) or Eradication binary, see the information below to resolve the issue.

If you need assistance, you can contact support for help with solving issues. You can contact Institute for Disease Modeling (IDM) support at support@idmod.org. When submitting the issue, please include any error information.

Unknown compiler version¶

If you encounter the warning “Unknown compiler version - please run the configure tests and report the results” when attempting to build the Eradication.exe or Eradication binary, this indicates you are using a version of Boost that is no longer supported. Install Boost 1.61.0.

Inconsistent DLL linkage¶

If you see the following warning on some files, “c:python27includepymath.h(22): warning C4273: ‘round’: inconsistent dll linkage”, this indicates that you are using a version of Python that is no longer supported. Install Python 2.7.11 or 2.7.12.

Error 255¶

Check to see if you have any white spaces in the path to your local EMOD source code. If you do, remove the white spaces in all of the directory names in the path.

Error LNK4272¶

If you see the error “LNK4272 library machine type ‘X86’ conflicts with target machine type ‘x64’”, this indicates that you need to uninstall 32-bit Python and reinstall 64-bit Python. Install Python 2.7.11 or 2.7.12.

Regression testing¶

After building the EMOD executable (Eradication.exe), it’s important to verify that Eradication.exe is performing properly. Regression testing is a method by which the built code is tested to see if it has “regressed” or moved backwards in any way, such as previously reported (and fixed) issues reappearing.

Within the EMOD Regression directory there are many subdirectories that correspond to different disease scenarios in a variety of locations. Each of these contains the configuration and campaign files needed to run the simulation and the reference output, which represents the expected results. These reference outputs have been calculated by the scientific researchers modeling each scenario.

EMOD regression testing consists of running one or more of these simulations and comparing the output to the reference output. If the two outputs are comparable, the test passes; if they differ, the test fails. Because EMOD is stochastic, a passing test will fall within some acceptable range of the reference output, rather than be an identical match. If a regression test fails, EMOD will produce a matplotlib chart of the first 16 channels in the InsetChart.json output report. You can then review the charts to identify the problem.

If you want to quickly compare a simulation output to the reference output, you can also run any of the regression scenarios as a typical simulation, as described in Run simulations. However, this will not include the comparison and pass/fail evaluation that regression_test.py conducts. In addition, if you choose to do this, be sure to specify a different output directory, such as “testing”, so as not to overwrite the reference output.

Run regression tests¶

The Python script regression_test.py runs a suite of regression simulations and compares the output to the reference output for each simulation. It is set up to run the simulations on an HPC cluster; however, you can run modify the script to run tests locally. However, the script was written with remote execution in mind and running it locally can be time-consuming. Running the entire regression suite locally will take several hours on average.

The regression scenarios, script, configuration file, and other relevant files are all in the Regression directory. Be aware that many of these tests, due to abnormally high or low values, will produce output that should not be considered scientifically accurate.

Modify the configuration file, regression_test.cfg, for your environment, setting the values for the location of the working directory, input data files, binary file, and cluster settings.
- For local Windows simulations, set the values under [WINDOWS].
- For local CentOS on Azure simulations, set the values under [POSIX]. Note that CentOS on Azure simulations are run locally by default and cannot be commissioned to an HPC cluster.
- For simulations on IDM HPC clusters, no changes are necessary if your username and password are cached locally.
- For simulations on your own HPC cluster, create [HPC-<cluster>] and [ENVIRONMENT-<cluster>] sections for your cluster that contain the same variables as shown for IDM HPC clusters.
Select the suite of regression tests you want to run. This is indicated by a JSON file in the following format:
```
{
    "tests": [{
        "path": "Relative path to test directory."
    }, {
        "path": "Relative path to test directory."
    }]
}
```
You can use one of the JSON files in the Regression directory or create your own. The sanity.json file is recommended for quickly testing a wide range of EMOD functionality.

From the Regression directory, open a Command Prompt window and run the regression test script, regression_test.py. It requires the name of the regression suite (without the .json extension) and the relative path to Eradication.exe. For example:

regression_test.py sanity ..\Eradication\x64\Release\Eradication.exe

In addition, you may need to include the following optional arguments depending on your testing environment or how Eradication.exe was built.

Argument	Default	Description
`--perf`	False	Measure Eradication.exe performance.
`--hidegraphs`	False	Suppress pop-up graphs in case of validation failures.
`--debug`	False	Use the debug path for EMODules.
`--label`		Add a custom suffix for HPC job name.
`--config`	regression_test.cfg	The regression test configuration file.
`--disable-schema-test`	True	Include to suppress schema testing, which is on by default.
`--use-dlls`	False	Use EMODules when running tests.
`--all-outputs`	False	Use all output JSON files for validation, not just InsetChart.json.
`--dll-path`		The path to the root directory of the EMODules to use.
`--skip-emodule-check`	False	Skip checking if EMODules on the cluster are up-to-date, which can be slow.
`--scons`	False	Indicate that this is a SCons build so custom DLLs are found in the build/64/Release directory.
`--local`	False	Run all simulations locally.

Review the output and examine any failures.

EMOD will output the standard error and logging files, StdErr.txt and StdOut.txt, produced from any simulation (see Error and logging files). In addition, regression_test.py will output time.txt under the regression test working directory and report_xxxx.xml under Regression/reports. The time report contains the EMOD version and total run time of the simulation. The regression report is in JUnit standard form and captures run information, including pass/fail/complete and time to complete.

If a simulation completes saying the run passed but the channel order was different than the reference output, this is considered a pass. However, if any output completes but does not match the reference output, this is considered a failure and a matplotlib chart of the output will appear in a pop-up window. The chart will appear immediately after the simulation, before the entire suite of regression tests completes. You can manipulate the output of the charts, such as adjusting the scale of the plots, zooming or panning, and so forth, through the icons at the bottom of the chart window.

If any of the regression tests fail and you have not made any changes to the EMOD source code, email support@idmod.org.

Create a new regression test¶

You can create a new regression based off one of the ones included with the EMOD source code using the steps below.

Under the Regression directory, create a new subdirectory.
Copy the contents of the regression test that you want to base the new test on into the subdirectory.
Modify the configuration, campaign, and demographic files as desired.
Create the reference output by doing one of the following:
- Modify the InsetChart.json file to match the output you expect.
- Run simulations until you have an acceptable InsetChart.json that you wish to use as the reference.